Analysis - Speech - All models
Tom Offrede
Summary of results
f0 change regardless of interlocutor (per speaker; using categorical values)
- Some participants significantly increased or decreased on different f0 measures to different extents, but there were almost no relationships with other predictors. This is the exception:
- First part of the experiment (Lists)
- f0 max decreased less often when the speaker felt closer to their partner.
- f0 max decreased less often when the speaker liked their partner more (small effect).
- f0 SD tended to increase more often when the person scored higher in neuroticism.
f0 change regardless of interlocutor (all speakers together; using continuous values)
- First section of the experiment (Lists)
- f0 median didn’t change in any condition.
- f0 max tended to decrease only in the impersonal condition, but if we’re being strict (AIC of model with condition was 1.7 smaller than without), there f0 max went down in both conditions.
- f0 SD decreased in both conditions.
- Second section of the experiment (Diapix)
- f0 median increased in both conditions.
- f0 max didn’t change significantly in either condition.
- f0 SD didn’t change in either condition.
- Across entire experiment
- f0 median increased in both conditions.
- f0 max decreased in both conditions.
- f0 SD didn’t change in either condition.
f0 entrainment
- Some participants entrained and some didn’t; sometimes only in the first part of the experiment, sometimes in the second, sometimes across it all.
- The most common effect was of dyssynchrony, meaning the participants often produce different f0 from the previous turn’s last IPU. Makes sense: question-answer.
- I didn’t find any effects of condition or relationship with temperature change or questionnaire responses.
Notes
- The check of residuals of logistic models below, was done following: https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html
- When analyzing the effect of temperature change (categorical, per speaker) on f0 change (categorical, per speaker), some effects were positive in the model; but some of the categories that produce “significance” have too few data points, so I don’t consider those “significant” results as actually meaningful or significant. A couple of times that that happens are indicated in the detailed analysis below, but after a few times that that happened I just ignored it as “non significant”.
Individual change – regardless of interlocutor
Individual analysis
ind <- data.frame(matrix(nrow=0, ncol=6))
names(ind) <- c("speaker", "condition", "feature", "section", "t", "p")for(s in unique(ipus$speaker)){
# f0 median
c <- tidy(lm(f0medz ~ turnOverall, ipus |> filter(speaker==s)))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0median", "entireExp",
c$statistic[2], c$p.value[2])
c <- tidy(lm(f0medz ~ turnOverall, ipus |> filter(speaker==s, task=="Lists")))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0median", "Lists",
c$statistic[2], c$p.value[2])
if(!all(is.na(ipus$f0medz[ipus$speaker==s & ipus$task=="Diapix"]))){ # need to do this because there are missing files for one dyad for this section
c <- tidy(lm(f0medz ~ turnOverall, ipus |> filter(speaker==s, task=="Diapix")))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0median", "Diapix",
c$statistic[2], c$p.value[2])
}
# f0 max
c <- tidy(lm(f0maxz ~ turnOverall, ipus |> filter(speaker==s)))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0max", "entireExp",
c$statistic[2], c$p.value[2])
c <- tidy(lm(f0maxz ~ turnOverall, ipus |> filter(speaker==s, task=="Lists")))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0max", "Lists",
c$statistic[2], c$p.value[2])
if(!all(is.na(ipus$f0maxz[ipus$speaker==s & ipus$task=="Diapix"]))){ # need to do this because there are missing files for one dyad for this section
c <- tidy(lm(f0maxz ~ turnOverall, ipus |> filter(speaker==s, task=="Diapix")))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0max", "Diapix",
c$statistic[2], c$p.value[2])
}
# f0 SD
c <- tidy(lm(f0sdz ~ turnOverall, ipus |> filter(speaker==s)))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0SD", "entireExp",
c$statistic[2], c$p.value[2])
c <- tidy(lm(f0sdz ~ turnOverall, ipus |> filter(speaker==s, task=="Lists")))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0SD", "Lists",
c$statistic[2], c$p.value[2])
if(!all(is.na(ipus$f0sdz[ipus$speaker==s & ipus$task=="Diapix"]))){ # need to do this because there are missing files for one dyad for this section
c <- tidy(lm(f0sdz ~ turnOverall, ipus |> filter(speaker==s, task=="Diapix")))
ind[nrow(ind)+1,] <- c(s, unique(ipus$condition[ipus$speaker==s]), "f0SD", "Diapix",
c$statistic[2], c$p.value[2])
}
}
dat <- merge(ind |>
mutate_at(c("t", "p"), as.numeric) |>
mutate(change = case_when(
p < 0.05 & t < 0 ~ "decrease",
p < 0.05 & t > 0 ~ "increase",
p > 0.05 ~ "ns"
)),
ipus |> select(speaker, gender, comfortPre, comfortPost, closeness, similarity, likeability, becomeFriends, extraversion, openness, agreeableness, conscientiousness, neuroticism) |> filter(!duplicated(speaker)),
by = "speaker") |>
mutate_at(c("speaker", "condition", "feature", "section", "change", "gender"), as.factor)
dat$change <- relevel(dat$change, ref="ns")# join individual temperature change effects
indTemp <- indTemp |>
mutate(effect = case_when(
effect == "ns" ~ "tempNS",
effect == "increase" ~ "tempIncrease",
effect == "decrease" ~ "tempDecrease",
))
s <- dat |>
mutate(change = case_when(
change == "ns" ~ "f0NS",
change == "increase" ~ "f0Increase",
change == "decrease" ~ "f0Decrease",
))
t1 <- indTemp |>
filter(section=="entireExp") |>
group_by(speaker) |>
pivot_wider(names_from = ROI, values_from = effect, id_cols=speaker) |>
mutate(section = "entireExp")
t2 <- indTemp |>
filter(section=="Lists") |>
group_by(speaker) |>
pivot_wider(names_from = ROI, values_from = effect, id_cols=speaker) |>
mutate(section = "Lists")
t3 <- indTemp |>
filter(section=="Diapix") |>
group_by(speaker) |>
pivot_wider(names_from = ROI, values_from = effect, id_cols=speaker) |>
mutate(section = "Diapix")
# mutate(section = "Lists") # naming the Diapix section and "Lists" to pair these values with the f0 adaptation during "Lists", since temperature change takes a while to happen
# this didn't bring about any new insights
t <- rbind(t1, rbind(t2, t3))
# t <- rbind(t1, t3)
ts <- merge(t,
s |>
select(-condition),
by=c("speaker", "section"), all=TRUE) |>
mutate_at(c("speaker", "section", "Forehead", "Eyes", "Nose", "Cheeks", "feature", "change", "gender"), as.factor)
ts$Forehead <- relevel(ts$Forehead, ref="tempNS")
ts$Cheeks <- relevel(ts$Cheeks, ref="tempNS")
ts$Eyes <- relevel(ts$Eyes, ref="tempNS")
ts$Nose <- relevel(ts$Nose, ref="tempNS")
ts$change <- relevel(ts$change, ref="f0NS")Explore possible predictors
First part of the experiment (Lists)
f0 median
No effect of condition
ggplot(dat |> filter(feature=="f0median", section=="Lists"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 median (first part of exp - Lists)")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 7.962800
## iter 20 value 7.835155
## iter 30 value 7.832797
## iter 40 value 7.832516
## iter 40 value 7.832516
## iter 40 value 7.832516
## final value 7.832516
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00000559 97.0 -0.125 0.901
## 2 decrease conditionimpersonal 1.00 97.0 0.0954 0.924
## 3 increase (Intercept) 0.0500 1.03 -2.87 0.00411
## 4 increase conditionimpersonal 0.0000450 158. -0.0635 0.949emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 9.50e-01 0.048735 4 0.814687 1.085308
## impersonal 9.44e-01 0.053990 4 0.794545 1.094345
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 5.30e-06 0.000515 4 -0.001425 0.001436
## impersonal 5.56e-02 0.053989 4 -0.094345 0.205450
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 5.00e-02 0.048733 4 -0.085306 0.185301
## impersonal 2.20e-06 0.000352 4 -0.000976 0.000981
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.00555 0.0727 4 0.076 0.9428
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.05555 0.0540 4 -1.029 0.3617
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.05000 0.0487 4 1.026 0.3629No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 8.110318
## iter 20 value 7.931639
## iter 30 value 7.931302
## final value 7.931302
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.168 2.07 -0.773 0.439
## 2 decrease closeness 0.325 0.880 -0.832 0.405
## 3 increase (Intercept) 0.000858 3.95 -1.79 0.0742
## 4 increase closeness 0.671 0.647 1.10 0.271tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 9.028983
## iter 20 value 8.448229
## iter 30 value 8.441630
## final value 8.441627
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.321 3.03 -0.247 0.805
## 2 decrease similarity 0.356 0.704 -0.845 0.398
## 3 increase (Intercept) 0.000961 5.20 -1.34 0.181
## 4 increase similarity 0.629 0.732 0.721 0.471tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 8.964779
## iter 20 value 8.844134
## final value 8.843673
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0155 5.15 -0.807 0.420
## 2 decrease likeability 0.521 0.721 0.114 0.909
## 3 increase (Intercept) 0.000111 7.91 -1.15 0.249
## 4 increase likeability 0.677 0.984 0.751 0.453tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 8.996318
## final value 8.770797
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00132 5.37 -1.24 0.217
## 2 decrease becomeFriends 0.615 0.756 0.621 0.534
## 3 increase (Intercept) 0.00132 5.37 -1.24 0.217
## 4 increase becomeFriends 0.615 0.756 0.621 0.534No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 7.850898
## iter 20 value 7.354077
## iter 30 value 7.347693
## iter 40 value 7.347368
## final value 7.347367
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 1.00 13.7 0.921 0.357
## 2 decrease extraversion 0.00380 5.18 -1.08 0.282
## 3 increase (Intercept) 0.904 6.97 0.321 0.748
## 4 increase extraversion 0.141 2.29 -0.790 0.430tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 9.012340
## final value 9.012328
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.165 4.18 -0.389 0.697
## 2 decrease openness 0.358 1.28 -0.458 0.647
## 3 increase (Intercept) 0.165 4.18 -0.389 0.697
## 4 increase openness 0.358 1.28 -0.458 0.647tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 7.676414
## iter 20 value 6.349851
## iter 30 value 6.148420
## iter 40 value 6.087807
## iter 50 value 6.035148
## iter 60 value 5.984933
## iter 70 value 5.954964
## iter 80 value 5.931183
## iter 90 value 5.915486
## iter 100 value 5.896061
## final value 5.896061
## stopped after 100 iterations## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 1 45.4 0.942 0.346
## 2 decrease agreeableness 0.0000000557 17.4 -0.959 0.337
## 3 increase (Intercept) 0.816 8.04 0.185 0.853
## 4 increase agreeableness 0.174 2.52 -0.617 0.537tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 9.453357
## iter 20 value 8.842892
## iter 30 value 8.826723
## final value 8.826014
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00000425 12.5 -0.992 0.321
## 2 decrease conscientiousness 0.887 2.80 0.736 0.461
## 3 increase (Intercept) 0.0999 6.59 -0.334 0.739
## 4 increase conscientiousness 0.413 1.67 -0.210 0.834tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0median", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 6.437604
## iter 20 value 6.132349
## iter 30 value 6.130790
## final value 6.130780
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00000197 9.02 -1.46 0.145
## 2 decrease neuroticism 0.930 2.19 1.18 0.239
## 3 increase (Intercept) 0.999 7.24 1.00 0.317
## 4 increase neuroticism 0.00984 3.70 -1.25 0.212No effect of forehead temperature change on f0 median change
ggplot(ts |> filter(section=="Lists", feature=="f0median", !is.na(Forehead)), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0median", section=="Lists", !is.na(Forehead)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 37.352818
## iter 10 value 7.935794
## iter 20 value 7.888776
## final value 7.887962
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.000000291 514. -0.0293 0.977
## 2 f0Decrease ForeheadtempDecrease 0.223 3497. -0.000357 1.00
## 3 f0Decrease ForeheadtempIncrease 1.00 514. 0.0237 0.981
## 4 f0Increase (Intercept) 0.000000291 514. -0.0293 0.977
## 5 f0Increase ForeheadtempDecrease 0.223 3497. -0.000357 1.00
## 6 f0Increase ForeheadtempIncrease 1.00 514. 0.0237 0.981emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 1e+00 0.00021170 6 0.9994814 1.0005174
## tempDecrease 1e+00 0.00040886 6 0.9989994 1.0010003
## tempIncrease 9e-01 0.06708199 6 0.7358565 1.0641439
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 3e-07 0.00014970 6 -0.0003660 0.0003666
## tempDecrease 1e-07 0.00028910 6 -0.0007073 0.0007075
## tempIncrease 5e-02 0.04873393 6 -0.0692477 0.1692476
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 3e-07 0.00014970 6 -0.0003660 0.0003666
## tempDecrease 1e-07 0.00028910 6 -0.0007073 0.0007075
## tempIncrease 5e-02 0.04873393 6 -0.0692477 0.1692475
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -4.2e-07 0.000460 6 -0.001 1.0000
## tempNS - tempIncrease 1.0e-01 0.067082 6 1.491 0.3593
## tempDecrease - tempIncrease 1.0e-01 0.067083 6 1.491 0.3593
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 2.1e-07 0.000326 6 0.001 1.0000
## tempNS - tempIncrease -5.0e-02 0.048734 6 -1.026 0.5890
## tempDecrease - tempIncrease -5.0e-02 0.048735 6 -1.026 0.5890
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 2.1e-07 0.000326 6 0.001 1.0000
## tempNS - tempIncrease -5.0e-02 0.048734 6 -1.026 0.5890
## tempDecrease - tempIncrease -5.0e-02 0.048735 6 -1.026 0.5890
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 median change
ggplot(ts |> filter(section=="Lists", feature=="f0median", !is.na(Nose)), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0median", section=="Lists", !is.na(Nose)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 37.352818
## iter 10 value 7.483791
## iter 20 value 7.275418
## iter 30 value 7.268836
## iter 30 value 7.268836
## iter 30 value 7.268836
## final value 7.268836
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.000000827 348. -0.0403 0.968
## 2 f0Decrease NosetempDecrease 0.0621 2166. -0.00125 0.999
## 3 f0Decrease NosetempIncrease 1.00 348. 0.0320 0.975
## 4 f0Increase (Intercept) 0.0909 1.05 -2.20 0.0281
## 5 f0Increase NosetempDecrease 0.0000145 415. -0.0268 0.979
## 6 f0Increase NosetempIncrease 0.0000976 75.5 -0.122 0.903emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.9091090 0.0866707 6 0.6970335 1.1211845
## tempDecrease 0.9999985 0.0006134 6 0.9984977 1.0014993
## tempIncrease 0.9473564 0.0512333 6 0.8219930 1.0727199
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.0000008 0.0002614 6 -0.0006389 0.0006404
## tempDecrease 0.0000001 0.0001169 6 -0.0002861 0.0002862
## tempIncrease 0.0526343 0.0512291 6 -0.0727187 0.1779874
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.0908903 0.0866703 6 -0.1211844 0.3029650
## tempDecrease 0.0000015 0.0006021 6 -0.0014719 0.0014748
## tempIncrease 0.0000092 0.0006974 6 -0.0016974 0.0017158
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -9.09e-02 0.086673 6 -1.049 0.5764
## tempNS - tempIncrease -3.82e-02 0.100681 6 -0.380 0.9245
## tempDecrease - tempIncrease 5.26e-02 0.051237 6 1.027 0.5882
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.00e-07 0.000286 6 0.002 1.0000
## tempNS - tempIncrease -5.26e-02 0.051230 6 -1.027 0.5882
## tempDecrease - tempIncrease -5.26e-02 0.051229 6 -1.027 0.5882
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 9.09e-02 0.086672 6 1.049 0.5764
## tempNS - tempIncrease 9.09e-02 0.086673 6 1.049 0.5765
## tempDecrease - tempIncrease -7.79e-06 0.000921 6 -0.008 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 median change
ggplot(ts |> filter(section=="Lists", feature=="f0median", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0median", section=="Lists", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 31.859756
## iter 10 value 7.321812
## iter 20 value 7.276667
## final value 7.276414
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0000000929 900. -0.0180 0.986
## 2 f0Decrease EyestempDecrease 0.210 37.5 -0.0353 0.972
## 3 f0Decrease EyestempIncrease 1.00 900. 0.0151 0.988
## 4 f0Increase (Intercept) 0.0000000929 900. -0.0180 0.986
## 5 f0Increase EyestempDecrease 0.210 37.5 -0.0353 0.972
## 6 f0Increase EyestempIncrease 1.00 900. 0.0151 0.988emmeans(m, pairwise~Eyes, by="change") # isn't working (I'm thinking maybe because "eyes" have lots of missing values)## $emmeans
## change = f0NS:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.9999998 0.00011824 6 0.9997105 1.0002891
## tempDecrease 1.0000000 0.00003271 6 0.9999199 1.0000800
## tempIncrease 0.8666546 0.08777411 6 0.6518791 1.0814301
##
## change = f0Decrease:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.0000001 0.00008361 6 -0.0002045 0.0002047
## tempDecrease 0.0000000 0.00002313 6 -0.0000566 0.0000566
## tempIncrease 0.0666727 0.06440883 6 -0.0909300 0.2242754
##
## change = f0Increase:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.0000001 0.00008361 6 -0.0002045 0.0002047
## tempDecrease 0.0000000 0.00002313 6 -0.0000566 0.0000566
## tempIncrease 0.0666727 0.06440882 6 -0.0909300 0.2242754
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -1.40e-07 8.55e-05 6 -0.002 1.0000
## tempNS - tempIncrease 1.33e-01 8.78e-02 6 1.519 0.3476
## tempDecrease - tempIncrease 1.33e-01 8.78e-02 6 1.519 0.3476
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.00e-08 6.05e-05 6 0.001 1.0000
## tempNS - tempIncrease -6.67e-02 6.44e-02 6 -1.035 0.5839
## tempDecrease - tempIncrease -6.67e-02 6.44e-02 6 -1.035 0.5839
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.00e-08 6.05e-05 6 0.001 1.0000
## tempNS - tempIncrease -6.67e-02 6.44e-02 6 -1.035 0.5839
## tempDecrease - tempIncrease -6.67e-02 6.44e-02 6 -1.035 0.5839
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of cheek temperature change on f0 median change
ggplot(ts |> filter(section=="Lists", feature=="f0median", , !is.na(Cheeks)), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0median", section=="Lists", !is.na(Cheeks)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 37.352818
## iter 10 value 7.282132
## iter 20 value 7.157311
## final value 7.127866
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.00000415 105. -0.118 0.906
## 2 f0Decrease CheekstempDecrease 0.166 644. -0.00251 0.998
## 3 f0Decrease CheekstempIncrease 1.00 105. 0.0998 0.920
## 4 f0Increase (Intercept) 0.0435 1.02 -3.02 0.00250
## 5 f0Increase CheekstempDecrease 0.000302 156. -0.0520 0.959
## 6 f0Increase CheekstempIncrease 0.000429 85.6 -0.0906 0.928emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.9565257 0.0425207 6 0.852481 1.060570
## tempDecrease 0.9999854 0.0022024 6 0.994596 1.005375
## tempIncrease 0.8749864 0.1169322 6 0.588864 1.161109
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.0000040 0.0004155 6 -0.001013 0.001021
## tempDecrease 0.0000008 0.0005244 6 -0.001282 0.001284
## tempIncrease 0.1249965 0.1169254 6 -0.161110 0.411103
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.0434703 0.0425189 6 -0.060570 0.147510
## tempDecrease 0.0000137 0.0021391 6 -0.005220 0.005248
## tempIncrease 0.0000171 0.0014607 6 -0.003557 0.003591
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -4.35e-02 0.042578 6 -1.021 0.5919
## tempNS - tempIncrease 8.15e-02 0.124423 6 0.655 0.7964
## tempDecrease - tempIncrease 1.25e-01 0.116953 6 1.069 0.5653
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 3.15e-06 0.000669 6 0.005 1.0000
## tempNS - tempIncrease -1.25e-01 0.116926 6 -1.069 0.5652
## tempDecrease - tempIncrease -1.25e-01 0.116927 6 -1.069 0.5652
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 4.35e-02 0.042573 6 1.021 0.5919
## tempNS - tempIncrease 4.35e-02 0.042544 6 1.021 0.5915
## tempDecrease - tempIncrease -3.34e-06 0.002590 6 -0.001 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesf0 max
No effect of condition
ggplot(dat |> filter(feature=="f0max", section=="Lists"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 max (first part of exp - Lists)")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 17.987773
## iter 20 value 17.963267
## iter 30 value 17.959908
## iter 40 value 17.959373
## iter 40 value 17.959372
## iter 40 value 17.959372
## final value 17.959372
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0000168 57.5 -0.191 0.848
## 2 decrease conditionimpersonal 1.00 57.5 0.179 0.858
## 3 increase (Intercept) 0.100 0.745 -2.95 0.00320
## 4 increase conditionimpersonal 0.000117 80.1 -0.113 0.910emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 9.00e-01 0.067085 4 0.71374 1.08625
## impersonal 6.67e-01 0.111110 4 0.35818 0.97517
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 1.51e-05 0.000869 4 -0.00240 0.00243
## impersonal 3.33e-01 0.111110 4 0.02483 0.64181
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 1.00e-01 0.067080 4 -0.08625 0.28624
## impersonal 8.70e-06 0.000694 4 -0.00192 0.00193
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.233 0.1298 4 1.798 0.1466
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.333 0.1111 4 -3.000 0.0400
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.100 0.0671 4 1.490 0.2104f0 max decreased less when the speaker felt closer to their partner.
ggplot(dat |> filter(feature=="f0max", section=="Lists"), aes(change, closeness))+
geom_boxplot()
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 19.290986
## final value 19.290383
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.813 1.18 1.24 0.214
## 2 decrease closeness 0.244 0.498 -2.27 0.0229
## 3 increase (Intercept) 0.0651 1.81 -1.47 0.141
## 4 increase closeness 0.497 0.414 -0.0259 0.979f0 max also decreased less (smaller effect) when the speaker liked their partner more.
ggplot(dat |> filter(feature=="f0max", section=="Lists"), aes(change, likeability))+
geom_boxplot()
tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 21.537138
## final value 21.412029
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.955 2.23 1.37 0.171
## 2 decrease likeability 0.325 0.366 -2.00 0.0452
## 3 increase (Intercept) 0.00989 4.51 -1.02 0.307
## 4 increase likeability 0.565 0.595 0.438 0.662tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 23.588881
## final value 23.588845
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.324 1.42 -0.519 0.604
## 2 decrease similarity 0.460 0.256 -0.629 0.529
## 3 increase (Intercept) 0.0111 3.05 -1.47 0.141
## 4 increase similarity 0.572 0.458 0.637 0.524tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 23.867638
## final value 23.867377
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.260 1.51 -0.691 0.490
## 2 decrease becomeFriends 0.476 0.252 -0.386 0.700
## 3 increase (Intercept) 0.0189 3.13 -1.26 0.207
## 4 increase becomeFriends 0.550 0.471 0.423 0.672No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 23.153713
## final value 23.104344
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.879 2.83 0.699 0.485
## 2 decrease extraversion 0.254 0.869 -1.24 0.215
## 3 increase (Intercept) 0.0191 4.52 -0.872 0.383
## 4 increase extraversion 0.585 1.23 0.280 0.780tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 23.881954
## final value 23.878063
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.274 1.96 -0.496 0.620
## 2 decrease openness 0.455 0.552 -0.328 0.743
## 3 increase (Intercept) 0.253 3.06 -0.354 0.723
## 4 increase openness 0.383 0.910 -0.524 0.600tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 22.303008
## iter 20 value 22.293775
## final value 22.293680
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.989 3.52 1.29 0.196
## 2 decrease agreeableness 0.135 1.09 -1.70 0.0895
## 3 increase (Intercept) 0.214 5.17 -0.251 0.802
## 4 increase agreeableness 0.400 1.49 -0.272 0.786tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 23.875699
## iter 20 value 23.856798
## iter 30 value 23.855926
## final value 23.855899
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.538 2.97 0.0518 0.959
## 2 decrease conscientiousness 0.391 0.750 -0.593 0.553
## 3 increase (Intercept) 0.0247 5.72 -0.642 0.521
## 4 increase conscientiousness 0.559 1.38 0.172 0.863tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0max", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 23.325217
## iter 20 value 23.257297
## final value 23.257293
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0186 2.28 -1.74 0.0817
## 2 decrease neuroticism 0.673 0.661 1.09 0.274
## 3 increase (Intercept) 0.00627 3.72 -1.36 0.173
## 4 increase neuroticism 0.673 1.07 0.678 0.498No effect of forehead temperature change on f0 max change
ggplot(ts |> filter(section=="Lists", feature=="f0max", !is.na(Forehead)), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0max", section=="Lists", !is.na(Forehead)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 37.352818
## iter 10 value 17.414964
## iter 20 value 17.388859
## final value 17.386627
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.167 0.775 -2.08 0.0377
## 2 f0Decrease ForeheadtempDecrease 0.000729 82.8 -0.0872 0.930
## 3 f0Decrease ForeheadtempIncrease 0.469 0.996 -0.126 0.900
## 4 f0Increase (Intercept) 0.0909 1.05 -2.20 0.0281
## 5 f0Increase ForeheadtempDecrease 0.0000754 364. -0.0261 0.979
## 6 f0Increase ForeheadtempIncrease 0.0000561 102. -0.0956 0.924emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 7.69e-01 0.116852 6 0.48332 1.05517
## tempDecrease 1.00e+00 0.012383 6 0.96955 1.03015
## tempIncrease 8.50e-01 0.079846 6 0.65461 1.04536
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 1.54e-01 0.100066 6 -0.09102 0.39869
## tempDecrease 1.46e-04 0.012074 6 -0.02940 0.02969
## tempIncrease 1.50e-01 0.079845 6 -0.04537 0.34538
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 7.69e-02 0.073903 6 -0.10392 0.25775
## tempDecrease 7.50e-06 0.002746 6 -0.00671 0.00673
## tempIncrease 4.80e-06 0.000488 6 -0.00119 0.00120
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -2.31e-01 0.11751 6 -1.962 0.2023
## tempNS - tempIncrease -8.07e-02 0.14153 6 -0.571 0.8402
## tempDecrease - tempIncrease 1.50e-01 0.08080 6 1.855 0.2315
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.54e-01 0.10079 6 1.525 0.3453
## tempNS - tempIncrease 3.83e-03 0.12802 6 0.030 0.9995
## tempDecrease - tempIncrease -1.50e-01 0.08075 6 -1.856 0.2312
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.69e-02 0.07395 6 1.040 0.5812
## tempNS - tempIncrease 7.69e-02 0.07390 6 1.041 0.5808
## tempDecrease - tempIncrease 2.78e-06 0.00279 6 0.001 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 max change
ggplot(ts |> filter(section=="Lists", feature=="f0max", !is.na(Nose)), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0max", section=="Lists", !is.na(Nose)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 37.352818
## iter 10 value 15.423706
## iter 20 value 15.382501
## iter 30 value 15.378962
## iter 30 value 15.378961
## iter 30 value 15.378961
## final value 15.378961
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0909 1.05 -2.20 0.0281
## 2 f0Decrease NosetempDecrease 0.0000403 288. -0.0352 0.972
## 3 f0Decrease NosetempIncrease 0.727 1.19 0.824 0.410
## 4 f0Increase (Intercept) 0.0000175 75.6 -0.145 0.885
## 5 f0Increase NosetempDecrease 1.00 75.6 0.130 0.896
## 6 f0Increase NosetempIncrease 0.0926 208. -0.0110 0.991emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 9.09e-01 0.086685 6 0.696966 1.121186
## tempDecrease 7.50e-01 0.216499 6 0.220270 1.279779
## tempIncrease 7.89e-01 0.093529 6 0.560616 1.018330
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 9.09e-02 0.086678 6 -0.121185 0.303001
## tempDecrease 3.00e-06 0.000869 6 -0.002124 0.002130
## tempIncrease 2.11e-01 0.093529 6 -0.018331 0.439383
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 1.59e-05 0.001202 6 -0.002925 0.002957
## tempDecrease 2.50e-01 0.216498 6 -0.279780 0.779725
## tempIncrease 1.40e-06 0.000272 6 -0.000665 0.000668
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.59e-01 0.23321 6 0.682 0.7821
## tempNS - tempIncrease 1.20e-01 0.12752 6 0.938 0.6385
## tempDecrease - tempIncrease -3.94e-02 0.23584 6 -0.167 0.9847
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 9.09e-02 0.08668 6 1.049 0.5764
## tempNS - tempIncrease -1.20e-01 0.12752 6 -0.938 0.6384
## tempDecrease - tempIncrease -2.11e-01 0.09353 6 -2.251 0.1403
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -2.50e-01 0.21650 6 -1.155 0.5191
## tempNS - tempIncrease 1.45e-05 0.00123 6 0.012 0.9999
## tempDecrease - tempIncrease 2.50e-01 0.21650 6 1.155 0.5191
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 max change
ggplot(ts |> filter(section=="Lists", feature=="f0max", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0max", section=="Lists", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 31.859756
## iter 10 value 15.760813
## iter 20 value 15.675517
## iter 30 value 15.666739
## iter 40 value 15.666431
## final value 15.666394
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0833 1.04 -2.30 0.0217
## 2 f0Decrease EyestempDecrease 0.000551 141. -0.0531 0.958
## 3 f0Decrease EyestempIncrease 0.800 1.20 1.16 0.247
## 4 f0Increase (Intercept) 0.0833 1.04 -2.30 0.0217
## 5 f0Increase EyestempDecrease 0.000527 144. -0.0522 0.958
## 6 f0Increase EyestempIncrease 0.0000750 115. -0.0823 0.934emmeans(m, pairwise~Eyes, by="change") # isn't working (I'm thinking maybe because "eyes" have lots of missing values)## $emmeans
## change = f0NS:
## Eyes prob SE df lower.CL upper.CL
## tempNS 8.46e-01 0.100065 6 0.60132 1.09102
## tempDecrease 1.00e+00 0.009900 6 0.97568 1.02413
## tempIncrease 7.33e-01 0.114181 6 0.45394 1.01272
##
## change = f0Decrease:
## Eyes prob SE df lower.CL upper.CL
## tempNS 7.69e-02 0.073903 6 -0.10392 0.25775
## tempDecrease 5.01e-05 0.007078 6 -0.01727 0.01737
## tempIncrease 2.67e-01 0.114180 6 -0.01272 0.54606
##
## change = f0Increase:
## Eyes prob SE df lower.CL upper.CL
## tempNS 7.69e-02 0.073901 6 -0.10392 0.25774
## tempDecrease 4.79e-05 0.006922 6 -0.01689 0.01699
## tempIncrease 5.00e-06 0.000577 6 -0.00141 0.00142
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -1.54e-01 0.10055 6 -1.529 0.3437
## tempNS - tempIncrease 1.13e-01 0.15182 6 0.743 0.7485
## tempDecrease - tempIncrease 2.67e-01 0.11461 6 2.326 0.1275
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.69e-02 0.07424 6 1.035 0.5838
## tempNS - tempIncrease -1.90e-01 0.13601 6 -1.395 0.4008
## tempDecrease - tempIncrease -2.67e-01 0.11440 6 -2.331 0.1267
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.69e-02 0.07422 6 1.036 0.5837
## tempNS - tempIncrease 7.69e-02 0.07390 6 1.041 0.5808
## tempDecrease - tempIncrease 4.29e-05 0.00695 6 0.006 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of cheek temperature change on f0 max change
ggplot(ts |> filter(section=="Lists", feature=="f0max", !is.na(Cheeks)), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0max", section=="Lists", !is.na(Cheeks)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 37.352818
## iter 10 value 17.414929
## iter 20 value 17.378473
## final value 17.375169
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.136 0.621 -2.97 0.00297
## 2 f0Decrease CheekstempDecrease 0.000355 77.1 -0.103 0.918
## 3 f0Decrease CheekstempIncrease 0.679 1.03 0.729 0.466
## 4 f0Increase (Intercept) 0.0500 1.03 -2.87 0.00411
## 5 f0Increase CheekstempDecrease 0.0000915 263. -0.0353 0.972
## 6 f0Increase CheekstempIncrease 0.000366 93.0 -0.0851 0.932emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 8.26e-01 0.07903 6 0.63274 1.0195
## tempDecrease 1.00e+00 0.00451 6 0.98891 1.0110
## tempIncrease 7.50e-01 0.15310 6 0.37536 1.1246
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 1.30e-01 0.07022 6 -0.04140 0.3022
## tempDecrease 5.61e-05 0.00432 6 -0.01052 0.0106
## tempIncrease 2.50e-01 0.15310 6 -0.12460 0.6246
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 4.35e-02 0.04252 6 -0.06057 0.1475
## tempDecrease 4.80e-06 0.00127 6 -0.00309 0.0031
## tempIncrease 1.45e-05 0.00134 6 -0.00328 0.0033
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -1.74e-01 0.07916 6 -2.196 0.1505
## tempNS - tempIncrease 7.61e-02 0.17229 6 0.442 0.8997
## tempDecrease - tempIncrease 2.50e-01 0.15316 6 1.632 0.3041
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.30e-01 0.07035 6 1.853 0.2320
## tempNS - tempIncrease -1.20e-01 0.16843 6 -0.710 0.7668
## tempDecrease - tempIncrease -2.50e-01 0.15316 6 -1.632 0.3041
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 4.35e-02 0.04254 6 1.022 0.5913
## tempNS - tempIncrease 4.35e-02 0.04254 6 1.021 0.5915
## tempDecrease - tempIncrease -9.65e-06 0.00185 6 -0.005 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesf0 SD
No effect of condition
ggplot(dat |> filter(feature=="f0SD", section=="Lists"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 SD (first part of exp - Lists)")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 21.010425
## iter 20 value 20.999217
## final value 20.999201
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.105 0.748 -2.86 0.00420
## 2 decrease conditionimpersonal 0.766 0.914 1.30 0.195
## 3 increase (Intercept) 0.0555 1.03 -2.75 0.00590
## 4 increase conditionimpersonal 0.000413 56.3 -0.138 0.890emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 8.50e-01 0.079835 4 0.62838 1.07170
## impersonal 7.22e-01 0.105571 4 0.42912 1.01534
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 1.00e-01 0.067075 4 -0.08625 0.28621
## impersonal 2.78e-01 0.105569 4 -0.01535 0.57086
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 5.00e-02 0.048727 4 -0.08530 0.18527
## impersonal 1.76e-05 0.000988 4 -0.00272 0.00276
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.128 0.1324 4 0.966 0.3889
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.178 0.1251 4 -1.421 0.2283
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.050 0.0487 4 1.025 0.3632No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 20.497020
## final value 20.141788
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.601 0.956 0.427 0.670
## 2 decrease closeness 0.356 0.316 -1.88 0.0599
## 3 increase (Intercept) 0.0964 2.18 -1.03 0.305
## 4 increase closeness 0.417 0.636 -0.526 0.599tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 22.151231
## final value 22.151123
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.412 1.34 -0.266 0.790
## 2 decrease similarity 0.450 0.242 -0.833 0.405
## 3 increase (Intercept) 0.113 3.12 -0.662 0.508
## 4 increase similarity 0.438 0.586 -0.423 0.672tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 20.449188
## iter 20 value 20.419352
## final value 20.419310
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.934 2.06 1.29 0.199
## 2 decrease likeability 0.347 0.327 -1.93 0.0533
## 3 increase (Intercept) 0.0461 5.30 -0.572 0.567
## 4 increase likeability 0.487 0.742 -0.0710 0.943tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 22.350595
## final value 22.346579
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.213 1.48 -0.887 0.375
## 2 decrease becomeFriends 0.494 0.241 -0.104 0.917
## 3 increase (Intercept) 0.00178 5.34 -1.19 0.236
## 4 increase becomeFriends 0.611 0.752 0.599 0.549f0 SD tended to increase more often when the person scored higher in neuroticism.
ggplot(dat |> filter(feature=="f0max", section=="Lists"), aes(change, neuroticism))+
geom_boxplot()
tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 21.441714
## iter 20 value 21.440212
## final value 21.440207
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0110 2.25 -2.00 0.0460
## 2 decrease neuroticism 0.716 0.645 1.43 0.153
## 3 increase (Intercept) 0.0233 4.61 -0.811 0.417
## 4 increase neuroticism 0.527 1.44 0.0753 0.940tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 20.611431
## iter 20 value 20.553190
## iter 30 value 20.550845
## final value 20.550716
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.981 2.91 1.36 0.175
## 2 decrease extraversion 0.162 0.911 -1.80 0.0720
## 3 increase (Intercept) 0.0514 6.24 -0.467 0.640
## 4 increase extraversion 0.466 1.76 -0.0784 0.938tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 22.166485
## final value 22.148128
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.425 1.83 -0.164 0.870
## 2 decrease openness 0.418 0.523 -0.635 0.525
## 3 increase (Intercept) 0.403 4.16 -0.0948 0.924
## 4 increase openness 0.286 1.33 -0.685 0.493tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 22.627857
## iter 20 value 22.373307
## final value 22.373224
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.400 2.89 -0.141 0.888
## 2 decrease agreeableness 0.424 0.841 -0.364 0.716
## 3 increase (Intercept) 0.00102 7.65 -0.901 0.368
## 4 increase agreeableness 0.725 2.05 0.475 0.635tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0SD", section=="Lists"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 21.216056
## iter 20 value 21.200688
## iter 30 value 21.199622
## iter 40 value 21.199512
## final value 21.199508
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.958 2.83 1.11 0.267
## 2 decrease conscientiousness 0.236 0.731 -1.61 0.108
## 3 increase (Intercept) 0.338 6.79 -0.0993 0.921
## 4 increase conscientiousness 0.336 1.72 -0.396 0.692No effect of forehead temperature change on f0 SD change
ggplot(ts |> filter(section=="Lists", feature=="f0SD", !is.na(Forehead)), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0SD", section=="Lists", !is.na(Forehead)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## Warning in nnet::multinom(change ~ Forehead, data = filter(ts, feature == :
## group 'f0Increase' is empty## # weights: 4 (3 variable)
## initial value 23.567004
## iter 10 value 17.072759
## iter 20 value 17.033512
## iter 30 value 17.031643
## final value 17.030886
## converged## # A tibble: 3 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 (Intercept) 0.231 0.658 -1.83 0.0674
## 2 1 ForeheadtempDecrease 0.000614 73.7 -0.100 0.920
## 3 1 ForeheadtempIncrease 0.455 0.864 -0.211 0.833emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.769247 0.1169 3 0.3974 1.1411
## tempDecrease 0.999816 0.0136 3 0.9566 1.0430
## tempIncrease 0.800001 0.0894 3 0.5154 1.0846
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.230753 0.1169 3 -0.1411 0.6026
## tempDecrease 0.000184 0.0136 3 -0.0430 0.0434
## tempIncrease 0.199999 0.0894 3 -0.0846 0.4846
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.2306 0.1176 3 -1.960 0.2682
## tempNS - tempIncrease -0.0308 0.1472 3 -0.209 0.9764
## tempDecrease - tempIncrease 0.1998 0.0905 3 2.209 0.2154
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.2306 0.1176 3 1.960 0.2682
## tempNS - tempIncrease 0.0308 0.1472 3 0.209 0.9764
## tempDecrease - tempIncrease -0.1998 0.0905 3 -2.209 0.2154
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 SD change
ggplot(ts |> filter(section=="Lists", feature=="f0SD", !is.na(Nose)), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0SD", section=="Lists", !is.na(Nose)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## Warning in nnet::multinom(change ~ Nose, data = filter(ts, feature == "f0SD", :
## group 'f0Increase' is empty## # weights: 4 (3 variable)
## initial value 23.567004
## iter 10 value 16.267922
## iter 20 value 16.227023
## iter 30 value 16.224701
## iter 40 value 16.224453
## iter 50 value 16.224165
## final value 16.224118
## converged## # A tibble: 3 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 (Intercept) 0.273 0.677 -1.45 0.147
## 2 1 NosetempDecrease 0.000153 66.1 -0.133 0.894
## 3 1 NosetempIncrease 0.416 0.880 -0.387 0.699emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 7.27e-01 0.13428 3 0.3000 1.1546
## tempDecrease 1.00e+00 0.00378 3 0.9879 1.0120
## tempIncrease 7.89e-01 0.09353 3 0.4918 1.0871
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 2.73e-01 0.13428 3 -0.1546 0.7000
## tempDecrease 5.73e-05 0.00378 3 -0.0120 0.0121
## tempIncrease 2.11e-01 0.09353 3 -0.0871 0.5082
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.2726 0.1343 3 -2.030 0.2521
## tempNS - tempIncrease -0.0622 0.1636 3 -0.380 0.9254
## tempDecrease - tempIncrease 0.2105 0.0936 3 2.248 0.2082
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.2726 0.1343 3 2.030 0.2521
## tempNS - tempIncrease 0.0622 0.1636 3 0.380 0.9254
## tempDecrease - tempIncrease -0.2105 0.0936 3 -2.248 0.2082
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 SD change (The model
actually suggests that f0 SD decreased more when the temperature also
decreased. But there are so few data points of
temperature decrease that I don’t think this is robust or
meaningful.)
ggplot(ts |> filter(section=="Lists", feature=="f0SD", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0SD", section=="Lists", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## Warning in nnet::multinom(change ~ Eyes, data = filter(ts, feature == "f0SD", :
## group 'f0Increase' is empty## # weights: 4 (3 variable)
## initial value 20.101268
## iter 10 value 12.252578
## iter 20 value 12.227911
## iter 30 value 12.225898
## iter 40 value 12.225215
## iter 50 value 12.224969
## iter 60 value 12.224617
## iter 70 value 12.224488
## final value 12.224478
## converged## # A tibble: 3 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 (Intercept) 0.0769 1.04 -2.39 0.0170
## 2 1 EyestempDecrease 1.00 58.9 0.181 0.857
## 3 1 EyestempIncrease 0.814 1.19 1.23 0.217emmeans(m, pairwise~Eyes, by="change") # isn't working (I'm thinking maybe because "eyes" have lots of missing values)## $emmeans
## change = f0NS:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.923064 0.0739 3 0.6878 1.1583
## tempDecrease 0.000289 0.0170 3 -0.0538 0.0543
## tempIncrease 0.733334 0.1142 3 0.3700 1.0967
##
## change = f0Decrease:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.076936 0.0739 3 -0.1583 0.3122
## tempDecrease 0.999711 0.0170 3 0.9457 1.0538
## tempIncrease 0.266666 0.1142 3 -0.0967 0.6300
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.923 0.0758 3 12.168 0.0025
## tempNS - tempIncrease 0.190 0.1360 3 1.395 0.4463
## tempDecrease - tempIncrease -0.733 0.1154 3 -6.350 0.0161
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.923 0.0758 3 -12.168 0.0025
## tempNS - tempIncrease -0.190 0.1360 3 -1.395 0.4463
## tempDecrease - tempIncrease 0.733 0.1154 3 6.350 0.0161
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of cheek temperature change on f0 SD change
ggplot(ts |> filter(section=="Lists", feature=="f0SD", !is.na(Cheeks)), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0SD", section=="Lists", !is.na(Cheeks)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## Warning in nnet::multinom(change ~ Cheeks, data = filter(ts, feature == : group
## 'f0Increase' is empty## # weights: 4 (3 variable)
## initial value 23.567004
## iter 10 value 15.940650
## iter 20 value 15.920784
## iter 30 value 15.920036
## iter 40 value 15.919828
## final value 15.919670
## converged## # A tibble: 3 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 (Intercept) 0.174 0.550 -2.83 0.00462
## 2 1 CheekstempDecrease 0.000499 56.3 -0.135 0.893
## 3 1 CheekstempIncrease 0.740 0.914 1.15 0.252emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.826097 0.07903 3 0.5746 1.0776
## tempDecrease 0.999895 0.00592 3 0.9811 1.0187
## tempIncrease 0.624995 0.17116 3 0.0803 1.1697
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.173903 0.07903 3 -0.0776 0.4254
## tempDecrease 0.000105 0.00592 3 -0.0187 0.0189
## tempIncrease 0.375005 0.17116 3 -0.1697 0.9197
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.174 0.0793 3 -2.193 0.2184
## tempNS - tempIncrease 0.201 0.1885 3 1.067 0.5928
## tempDecrease - tempIncrease 0.375 0.1713 3 2.189 0.2192
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.174 0.0793 3 2.193 0.2184
## tempNS - tempIncrease -0.201 0.1885 3 -1.067 0.5928
## tempDecrease - tempIncrease -0.375 0.1713 3 -2.189 0.2192
##
## P value adjustment: tukey method for comparing a family of 3 estimatesSecond part of the experiment (Diapix)
f0 median
No effect of condition
ggplot(dat |> filter(feature=="f0median", section=="Diapix"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 median (second part of exp - Diapix)")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 39.550042
## final value 22.941612
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.143 0.764 -2.35 0.0190
## 2 decrease conditionimpersonal 0.273 1.28 -0.765 0.445
## 3 increase (Intercept) 0.250 0.577 -1.90 0.0571
## 4 increase conditionimpersonal 0.158 1.18 -1.42 0.157emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 0.6667 0.1111 4 0.3582 0.975
## impersonal 0.8889 0.0741 4 0.6832 1.095
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 0.1111 0.0741 4 -0.0946 0.317
## impersonal 0.0556 0.0540 4 -0.0943 0.205
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 0.2222 0.0980 4 -0.0498 0.494
## impersonal 0.0556 0.0540 4 -0.0943 0.205
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.2222 0.1335 4 -1.664 0.1714
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.0556 0.0917 4 0.606 0.5772
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.1667 0.1119 4 1.490 0.2106No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 22.366200
## final value 22.363720
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.151 1.27 -1.36 0.174
## 2 decrease closeness 0.461 0.367 -0.428 0.669
## 3 increase (Intercept) 0.0199 1.48 -2.62 0.00867
## 4 increase closeness 0.623 0.286 1.75 0.0795tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 24.118930
## final value 24.118928
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0413 2.15 -1.46 0.144
## 2 decrease similarity 0.540 0.347 0.456 0.648
## 3 increase (Intercept) 0.0671 1.71 -1.54 0.125
## 4 increase similarity 0.539 0.277 0.570 0.569tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 24.140354
## iter 20 value 24.125622
## iter 20 value 24.125622
## iter 20 value 24.125622
## final value 24.125622
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0120 3.45 -1.28 0.201
## 2 decrease likeability 0.577 0.467 0.662 0.508
## 3 increase (Intercept) 0.132 2.36 -0.799 0.424
## 4 increase likeability 0.506 0.340 0.0716 0.943tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 23.489730
## final value 23.488963
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00332 3.23 -1.76 0.0776
## 2 decrease becomeFriends 0.632 0.461 1.17 0.240
## 3 increase (Intercept) 0.0911 1.80 -1.28 0.200
## 4 increase becomeFriends 0.525 0.290 0.340 0.734No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 23.964502
## iter 20 value 23.934691
## final value 23.934585
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0190 3.60 -1.10 0.273
## 2 decrease extraversion 0.620 0.995 0.493 0.622
## 3 increase (Intercept) 0.0167 2.93 -1.39 0.165
## 4 increase extraversion 0.661 0.802 0.832 0.405tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 23.891857
## iter 20 value 23.881221
## iter 20 value 23.881221
## final value 23.881221
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0559 2.78 -1.02 0.308
## 2 decrease openness 0.542 0.752 0.222 0.825
## 3 increase (Intercept) 0.0187 2.53 -1.57 0.117
## 4 increase openness 0.647 0.648 0.935 0.350tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 23.912500
## final value 23.903971
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.383 4.30 -0.111 0.912
## 2 decrease agreeableness 0.372 1.29 -0.406 0.685
## 3 increase (Intercept) 0.0106 3.61 -1.25 0.210
## 4 increase agreeableness 0.691 1.00 0.800 0.424tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 24.226126
## final value 24.220072
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00894 4.99 -0.944 0.345
## 2 decrease conscientiousness 0.647 1.19 0.509 0.611
## 3 increase (Intercept) 0.126 3.39 -0.571 0.568
## 4 increase conscientiousness 0.513 0.836 0.0642 0.949tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 21.246273
## final value 21.240362
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.963 2.84 1.15 0.250
## 2 decrease neuroticism 0.129 1.07 -1.78 0.0745
## 3 increase (Intercept) 0.890 2.25 0.928 0.354
## 4 increase neuroticism 0.221 0.774 -1.63 0.103No effect of forehead temperature change on f0 median change
ggplot(ts |> filter(section=="Diapix", feature=="f0median"), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 23.853671
## iter 20 value 23.834187
## final value 23.832013
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0714 1.04 -2.47 0.0134
## 2 f0Decrease ForeheadtempDecrease 0.00137 97.5 -0.0676 0.946
## 3 f0Decrease ForeheadtempIncrease 0.650 1.28 0.482 0.630
## 4 f0Increase (Intercept) 0.187 0.641 -2.29 0.0220
## 5 f0Increase ForeheadtempDecrease 0.000688 79.3 -0.0918 0.927
## 6 f0Increase ForeheadtempIncrease 0.382 0.991 -0.484 0.629emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.764745 0.1029 6 0.5130 1.0165
## tempDecrease 0.999736 0.0162 6 0.9600 1.0395
## tempIncrease 0.777777 0.0980 6 0.5380 1.0176
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.058837 0.0571 6 -0.0808 0.1985
## tempDecrease 0.000105 0.0103 6 -0.0250 0.0252
## tempIncrease 0.111105 0.0741 6 -0.0701 0.2924
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.176418 0.0924 6 -0.0498 0.4026
## tempDecrease 0.000159 0.0126 6 -0.0307 0.0310
## tempIncrease 0.111118 0.0741 6 -0.0701 0.2924
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.2350 0.1041 6 -2.256 0.1393
## tempNS - tempIncrease -0.0130 0.1421 6 -0.092 0.9954
## tempDecrease - tempIncrease 0.2220 0.0993 6 2.235 0.1432
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0587 0.0580 6 1.013 0.5963
## tempNS - tempIncrease -0.0523 0.0935 6 -0.559 0.8459
## tempDecrease - tempIncrease -0.1110 0.0748 6 -1.484 0.3620
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.1763 0.0933 6 1.889 0.2218
## tempNS - tempIncrease 0.0653 0.1185 6 0.551 0.8497
## tempDecrease - tempIncrease -0.1110 0.0751 6 -1.477 0.3652
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 median change
ggplot(ts |> filter(section=="Diapix", feature=="f0median"), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 23.191500
## iter 20 value 23.175791
## iter 30 value 23.174501
## final value 23.174500
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.125 1.07 -1.82 0.0687
## 2 f0Decrease NosetempDecrease 0.0000452 197. -0.0509 0.959
## 3 f0Decrease NosetempIncrease 0.452 1.30 -0.149 0.882
## 4 f0Increase (Intercept) 0.222 0.802 -1.56 0.118
## 5 f0Increase NosetempDecrease 0.0000985 94.3 -0.0979 0.922
## 6 f0Increase NosetempIncrease 0.382 1.02 -0.474 0.636emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 7.00e-01 0.14491 6 0.34543 1.05460
## tempDecrease 1.00e+00 0.00294 6 0.99277 1.00716
## tempIncrease 7.73e-01 0.08935 6 0.55411 0.99135
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 1.00e-01 0.09487 6 -0.13213 0.33213
## tempDecrease 6.50e-06 0.00127 6 -0.00310 0.00312
## tempIncrease 9.09e-02 0.06129 6 -0.05906 0.24089
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 2.00e-01 0.12649 6 -0.10952 0.50949
## tempDecrease 2.81e-05 0.00265 6 -0.00646 0.00652
## tempIncrease 1.36e-01 0.07316 6 -0.04267 0.31538
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.29995 0.1449 6 -2.069 0.1767
## tempNS - tempIncrease -0.07272 0.1702 6 -0.427 0.9058
## tempDecrease - tempIncrease 0.22723 0.0894 6 2.542 0.0968
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.09999 0.0949 6 1.054 0.5735
## tempNS - tempIncrease 0.00908 0.1129 6 0.080 0.9964
## tempDecrease - tempIncrease -0.09091 0.0613 6 -1.483 0.3626
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.19996 0.1265 6 1.581 0.3234
## tempNS - tempIncrease 0.06363 0.1461 6 0.435 0.9024
## tempDecrease - tempIncrease -0.13633 0.0732 6 -1.862 0.2294
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 median change
ggplot(ts |> filter(section=="Diapix", feature=="f0median", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0median", section=="Diapix", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 31.859756
## iter 10 value 18.287158
## iter 20 value 18.265923
## final value 18.264576
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0000229 66.1 -0.162 0.872
## 2 f0Decrease EyestempDecrease 1.00 66.1 0.162 0.872
## 3 f0Decrease EyestempIncrease 1.00 66.1 0.136 0.892
## 4 f0Increase (Intercept) 0.167 0.775 -2.08 0.0377
## 5 f0Increase EyestempDecrease 0.00000849 767. -0.0152 0.988
## 6 f0Increase EyestempIncrease 0.476 1.09 -0.0873 0.930emmeans(m, pairwise~Eyes, by="change")## $emmeans
## change = f0NS:
## Eyes prob SE df lower.CL upper.CL
## tempNS 8.33e-01 0.107593 6 0.57002 1.09656
## tempDecrease 5.00e-01 0.353553 6 -0.36510 1.36513
## tempIncrease 7.33e-01 0.114182 6 0.45393 1.01271
##
## change = f0Decrease:
## Eyes prob SE df lower.CL upper.CL
## tempNS 1.91e-05 0.001262 6 -0.00307 0.00311
## tempDecrease 5.00e-01 0.353553 6 -0.36513 1.36510
## tempIncrease 1.33e-01 0.087768 6 -0.08144 0.34809
##
## change = f0Increase:
## Eyes prob SE df lower.CL upper.CL
## tempNS 1.67e-01 0.107588 6 -0.09657 0.42995
## tempDecrease 8.00e-07 0.000652 6 -0.00159 0.00160
## tempIncrease 1.33e-01 0.087777 6 -0.08143 0.34814
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.3333 0.3696 6 0.902 0.6589
## tempNS - tempIncrease 0.1000 0.1569 6 0.637 0.8060
## tempDecrease - tempIncrease -0.2333 0.3715 6 -0.628 0.8108
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.5000 0.3536 6 -1.414 0.3923
## tempNS - tempIncrease -0.1333 0.0878 6 -1.519 0.3478
## tempDecrease - tempIncrease 0.3667 0.3643 6 1.007 0.5998
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.1667 0.1076 6 1.549 0.3355
## tempNS - tempIncrease 0.0333 0.1389 6 0.240 0.9689
## tempDecrease - tempIncrease -0.1334 0.0878 6 -1.519 0.3476
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of cheek temperature change on f0 median change
ggplot(ts |> filter(section=="Diapix", feature=="f0median"), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0median", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 24.066376
## iter 10 value 24.066376
## iter 10 value 24.066376
## final value 24.066376
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0625 1.03 -2.62 0.00874
## 2 f0Decrease CheekstempDecrease 0.682 1.49 0.513 0.608
## 3 f0Decrease CheekstempIncrease 0.714 1.49 0.613 0.540
## 4 f0Increase (Intercept) 0.167 0.632 -2.54 0.0109
## 5 f0Increase CheekstempDecrease 0.417 1.24 -0.271 0.786
## 6 f0Increase CheekstempIncrease 0.455 1.25 -0.146 0.884emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.7895 0.0935 6 0.5606 1.018
## tempDecrease 0.7778 0.1386 6 0.4387 1.117
## tempIncrease 0.7500 0.1531 6 0.3754 1.125
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.0526 0.0512 6 -0.0727 0.178
## tempDecrease 0.1111 0.1048 6 -0.1452 0.367
## tempIncrease 0.1250 0.1169 6 -0.1611 0.411
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.1579 0.0837 6 -0.0468 0.363
## tempDecrease 0.1111 0.1048 6 -0.1452 0.367
## tempIncrease 0.1250 0.1169 6 -0.1611 0.411
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0117 0.167 6 0.070 0.9973
## tempNS - tempIncrease 0.0395 0.179 6 0.220 0.9738
## tempDecrease - tempIncrease 0.0278 0.206 6 0.135 0.9901
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.0585 0.117 6 -0.501 0.8733
## tempNS - tempIncrease -0.0724 0.128 6 -0.567 0.8420
## tempDecrease - tempIncrease -0.0139 0.157 6 -0.088 0.9957
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0468 0.134 6 0.349 0.9358
## tempNS - tempIncrease 0.0329 0.144 6 0.229 0.9717
## tempDecrease - tempIncrease -0.0139 0.157 6 -0.088 0.9957
##
## P value adjustment: tukey method for comparing a family of 3 estimatesf0 max
No effect of condition
ggplot(dat |> filter(feature=="f0max", section=="Diapix"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 max (second part of exp - Diapix)")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.563952
## iter 20 value 19.554989
## iter 30 value 19.554804
## final value 19.554788
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.222 0.567 -2.21 0.0271
## 2 decrease conditionimpersonal 0.318 0.942 -0.809 0.419
## 3 increase (Intercept) 0.0000308 48.1 -0.216 0.829
## 4 increase conditionimpersonal 1.00 48.1 0.160 0.873emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 0.777757 0.09799 4 0.50568 1.04983
## impersonal 0.833352 0.08784 4 0.58948 1.07723
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 0.222219 0.09799 4 -0.04985 0.49428
## impersonal 0.111107 0.07407 4 -0.09455 0.31677
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 0.000024 0.00115 4 -0.00318 0.00323
## impersonal 0.055541 0.05398 4 -0.09434 0.20542
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.0556 0.132 4 -0.422 0.6944
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.1111 0.123 4 0.905 0.4169
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.0555 0.054 4 -1.028 0.3620No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 20.099267
## final value 19.953626
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.251 0.963 -1.14 0.256
## 2 decrease closeness 0.466 0.250 -0.542 0.588
## 3 increase (Intercept) 0.208 2.06 -0.650 0.516
## 4 increase closeness 0.324 0.871 -0.846 0.398tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.222474
## final value 19.218495
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.237 1.48 -0.792 0.428
## 2 decrease similarity 0.482 0.254 -0.283 0.777
## 3 increase (Intercept) 0.908 3.87 0.592 0.554
## 4 increase similarity 0.198 1.19 -1.18 0.239tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 17.570607
## iter 20 value 17.450132
## iter 30 value 17.394468
## iter 40 value 17.361615
## iter 50 value 17.334418
## iter 60 value 17.323287
## iter 70 value 17.310066
## iter 80 value 17.305319
## iter 90 value 17.297222
## iter 100 value 17.291670
## final value 17.291670
## stopped after 100 iterations## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0448 2.50 -1.22 0.221
## 2 decrease likeability 0.552 0.344 0.611 0.541
## 3 increase (Intercept) 1.00 27.1 0.841 0.400
## 4 increase likeability 0.00345 6.68 -0.848 0.396tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.177535
## final value 19.176834
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.214 1.61 -0.809 0.419
## 2 decrease becomeFriends 0.488 0.264 -0.176 0.861
## 3 increase (Intercept) 0.856 3.40 0.525 0.600
## 4 increase becomeFriends 0.229 1.04 -1.17 0.242No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.805220
## iter 20 value 19.794081
## final value 19.793509
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.840 2.74 0.604 0.546
## 2 decrease extraversion 0.276 0.835 -1.16 0.247
## 3 increase (Intercept) 0.319 6.03 -0.126 0.900
## 4 increase extraversion 0.316 1.83 -0.423 0.672tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 18.607126
## iter 20 value 17.697111
## iter 30 value 17.661712
## iter 40 value 17.634032
## iter 50 value 17.615169
## iter 60 value 17.601253
## iter 70 value 17.589783
## iter 80 value 17.579500
## iter 90 value 17.569641
## iter 100 value 17.559319
## final value 17.559319
## stopped after 100 iterations## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.245 2.05 -0.551 0.582
## 2 decrease openness 0.470 0.562 -0.214 0.830
## 3 increase (Intercept) 1.00 39.2 0.739 0.460
## 4 increase openness 0.000000480 19.3 -0.754 0.451tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.820397
## iter 20 value 19.602438
## iter 30 value 19.600692
## final value 19.600676
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.576 3.21 0.0951 0.924
## 2 decrease agreeableness 0.365 0.948 -0.584 0.559
## 3 increase (Intercept) 1.00 10.1 0.794 0.427
## 4 increase agreeableness 0.0250 3.49 -1.05 0.294tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 18.939842
## iter 20 value 18.429248
## final value 18.429138
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.540 3.15 0.0513 0.959
## 2 decrease conscientiousness 0.393 0.786 -0.551 0.582
## 3 increase (Intercept) 1.00 8.46 1.18 0.240
## 4 increase conscientiousness 0.0186 2.95 -1.34 0.179tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 20.073513
## iter 20 value 20.069541
## final value 20.069512
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.583 1.92 0.174 0.862
## 2 decrease neuroticism 0.349 0.630 -0.990 0.322
## 3 increase (Intercept) 0.116 4.31 -0.472 0.637
## 4 increase neuroticism 0.395 1.39 -0.307 0.759No effect of forehead temperature change on f0 max change
ggplot(ts |> filter(section=="Diapix", feature=="f0max"), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 18.005433
## iter 20 value 17.969731
## iter 30 value 17.964401
## iter 30 value 17.964401
## iter 30 value 17.964401
## final value 17.964401
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0588 1.03 -2.69 0.00715
## 2 f0Decrease ForeheadtempDecrease 0.00183 93.3 -0.0675 0.946
## 3 f0Decrease ForeheadtempIncrease 0.870 1.16 1.64 0.102
## 4 f0Increase (Intercept) 0.0000278 47.4 -0.221 0.825
## 5 f0Increase ForeheadtempDecrease 0.300 294. -0.00289 0.998
## 6 f0Increase ForeheadtempIncrease 1.00 47.4 0.169 0.866emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 9.41e-01 0.05708 6 0.80147 1.08082
## tempDecrease 1.00e+00 0.01126 6 0.97233 1.02741
## tempIncrease 6.67e-01 0.11111 6 0.39479 0.93855
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 5.88e-02 0.05707 6 -0.08082 0.19847
## tempDecrease 1.15e-04 0.01071 6 -0.02610 0.02633
## tempIncrease 2.78e-01 0.10557 6 0.01946 0.53611
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 2.62e-05 0.00124 6 -0.00301 0.00306
## tempDecrease 1.19e-05 0.00345 6 -0.00844 0.00846
## tempIncrease 5.55e-02 0.05399 6 -0.07655 0.18764
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -5.87e-02 0.05818 6 -1.009 0.5982
## tempNS - tempIncrease 2.74e-01 0.12492 6 2.197 0.1502
## tempDecrease - tempIncrease 3.33e-01 0.11168 6 2.984 0.0555
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 5.87e-02 0.05807 6 1.011 0.5972
## tempNS - tempIncrease -2.19e-01 0.12001 6 -1.824 0.2404
## tempDecrease - tempIncrease -2.78e-01 0.10611 6 -2.617 0.0880
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.43e-05 0.00367 6 0.004 1.0000
## tempNS - tempIncrease -5.55e-02 0.05400 6 -1.028 0.5878
## tempDecrease - tempIncrease -5.55e-02 0.05410 6 -1.027 0.5887
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 max change
ggplot(ts |> filter(section=="Diapix", feature=="f0max"), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 17.962791
## iter 20 value 17.851451
## iter 30 value 17.844235
## iter 30 value 17.844235
## iter 30 value 17.844235
## final value 17.844235
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0000490 45.2 -0.220 0.826
## 2 f0Decrease NosetempDecrease 1.00 45.2 0.195 0.845
## 3 f0Decrease NosetempIncrease 1.00 45.2 0.194 0.846
## 4 f0Increase (Intercept) 0.00000771 114. -0.103 0.918
## 5 f0Increase NosetempDecrease 0.0797 716. -0.00342 0.997
## 6 f0Increase NosetempIncrease 1.00 114. 0.0790 0.937emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.9999433 0.0023816 6 0.994116 1.005771
## tempDecrease 0.7500406 0.2164946 6 0.220297 1.279784
## tempIncrease 0.7272845 0.0949502 6 0.494950 0.959619
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.0000490 0.0022138 6 -0.005368 0.005466
## tempDecrease 0.2499589 0.2164945 6 -0.279784 0.779702
## tempIncrease 0.2272645 0.0893449 6 0.008645 0.445884
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.0000077 0.0008779 6 -0.002141 0.002156
## tempDecrease 0.0000005 0.0003537 6 -0.000865 0.000866
## tempIncrease 0.0454510 0.0444078 6 -0.063211 0.154113
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 2.50e-01 0.216508 6 1.154 0.5193
## tempNS - tempIncrease 2.73e-01 0.094980 6 2.871 0.0639
## tempDecrease - tempIncrease 2.28e-02 0.236401 6 0.096 0.9949
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -2.50e-01 0.216506 6 -1.154 0.5193
## tempNS - tempIncrease -2.27e-01 0.089372 6 -2.542 0.0967
## tempDecrease - tempIncrease 2.27e-02 0.234206 6 0.097 0.9948
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.21e-06 0.000947 6 0.008 1.0000
## tempNS - tempIncrease -4.54e-02 0.044416 6 -1.023 0.5906
## tempDecrease - tempIncrease -4.55e-02 0.044409 6 -1.023 0.5904
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 max change
ggplot(ts |> filter(section=="Diapix", feature=="f0max", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0max", section=="Diapix", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 31.859756
## iter 10 value 15.544436
## iter 20 value 15.496500
## iter 30 value 15.492336
## iter 40 value 15.491808
## iter 40 value 15.491808
## final value 15.491808
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0833 1.04 -2.30 0.0217
## 2 f0Decrease EyestempDecrease 0.000129 206. -0.0434 0.965
## 3 f0Decrease EyestempIncrease 0.815 1.20 1.23 0.217
## 4 f0Increase (Intercept) 0.00000245 193. -0.0670 0.947
## 5 f0Increase EyestempDecrease 0.134 1167. -0.00160 0.999
## 6 f0Increase EyestempIncrease 1.00 193. 0.0551 0.956emmeans(m, pairwise~Eyes, by="change")## $emmeans
## change = f0NS:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.9167358 0.0797555 6 0.721581 1.111890
## tempDecrease 0.9999879 0.0024629 6 0.993961 1.006014
## tempIncrease 0.6667512 0.1217084 6 0.368941 0.964561
##
## change = f0Decrease:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.0832620 0.0797545 6 -0.111890 0.278414
## tempDecrease 0.0000118 0.0024243 6 -0.005920 0.005944
## tempIncrease 0.2665892 0.1141693 6 -0.012773 0.545951
##
## change = f0Increase:
## Eyes prob SE df lower.CL upper.CL
## tempNS 0.0000022 0.0004323 6 -0.001056 0.001060
## tempDecrease 0.0000004 0.0004343 6 -0.001062 0.001063
## tempIncrease 0.0666596 0.0644029 6 -0.090929 0.224248
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -8.33e-02 0.079794 6 -1.043 0.5794
## tempNS - tempIncrease 2.50e-01 0.145512 6 1.718 0.2740
## tempDecrease - tempIncrease 3.33e-01 0.121733 6 2.737 0.0755
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 8.33e-02 0.079791 6 1.043 0.5794
## tempNS - tempIncrease -1.83e-01 0.139267 6 -1.316 0.4375
## tempDecrease - tempIncrease -2.67e-01 0.114195 6 -2.334 0.1261
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.90e-06 0.000613 6 0.003 1.0000
## tempNS - tempIncrease -6.67e-02 0.064404 6 -1.035 0.5840
## tempDecrease - tempIncrease -6.67e-02 0.064404 6 -1.035 0.5840
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of cheek temperature change on f0 max change
ggplot(ts |> filter(section=="Diapix", feature=="f0max"), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0max", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 19.684397
## iter 20 value 19.668362
## iter 30 value 19.666080
## iter 30 value 19.666080
## iter 30 value 19.666080
## final value 19.666080
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.167 0.632 -2.54 0.0109
## 2 f0Decrease CheekstempDecrease 0.385 1.23 -0.381 0.703
## 3 f0Decrease CheekstempIncrease 0.625 1.03 0.495 0.621
## 4 f0Increase (Intercept) 0.0625 1.03 -2.62 0.00874
## 5 f0Increase CheekstempDecrease 0.000208 94.9 -0.0894 0.929
## 6 f0Increase CheekstempIncrease 0.000121 144. -0.0627 0.950emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 7.89e-01 0.09353 6 0.56063 1.01834
## tempDecrease 8.89e-01 0.10476 6 0.63255 1.14522
## tempIncrease 7.50e-01 0.15309 6 0.37539 1.12460
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 1.58e-01 0.08366 6 -0.04680 0.36260
## tempDecrease 1.11e-01 0.10475 6 -0.14522 0.36742
## tempIncrease 2.50e-01 0.15309 6 -0.12461 0.62460
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 5.26e-02 0.05122 6 -0.07272 0.17795
## tempDecrease 1.24e-05 0.00117 6 -0.00285 0.00288
## tempIncrease 6.00e-06 0.00087 6 -0.00212 0.00213
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -9.94e-02 0.14043 6 -0.708 0.7680
## tempNS - tempIncrease 3.95e-02 0.17940 6 0.220 0.9738
## tempDecrease - tempIncrease 1.39e-01 0.18550 6 0.749 0.7455
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 4.68e-02 0.13406 6 0.349 0.9357
## tempNS - tempIncrease -9.21e-02 0.17446 6 -0.528 0.8609
## tempDecrease - tempIncrease -1.39e-01 0.18550 6 -0.749 0.7454
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 5.26e-02 0.05123 6 1.027 0.5886
## tempNS - tempIncrease 5.26e-02 0.05123 6 1.027 0.5884
## tempDecrease - tempIncrease 6.30e-06 0.00146 6 0.004 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesf0 SD
No effect of condition
ggplot(dat |> filter(feature=="f0SD", section=="Diapix"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 SD (second part of exp - Diapix)")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.907756
## iter 20 value 19.894618
## final value 19.894576
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.167 0.632 -2.54 0.0109
## 2 decrease conditionimpersonal 0.517 0.897 0.0768 0.939
## 3 increase (Intercept) 0.0000281 48.7 -0.215 0.830
## 4 increase conditionimpersonal 1.00 48.7 0.161 0.872emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 8.33e-01 0.08785 4 0.58942 1.07721
## impersonal 7.78e-01 0.09799 4 0.50574 1.04985
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 1.67e-01 0.08784 4 -0.07722 0.41055
## impersonal 1.67e-01 0.08784 4 -0.07722 0.41054
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 2.34e-05 0.00114 4 -0.00314 0.00319
## impersonal 5.55e-02 0.05399 4 -0.09434 0.20543
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal 5.55e-02 0.132 4 0.422 0.6948
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal 3.76e-06 0.124 4 0.000 1.0000
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal -5.55e-02 0.054 4 -1.028 0.3620No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 20.119696
## final value 20.091585
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.151 1.01 -1.72 0.0863
## 2 decrease closeness 0.510 0.237 0.170 0.865
## 3 increase (Intercept) 0.189 2.05 -0.710 0.478
## 4 increase closeness 0.331 0.869 -0.811 0.417tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 18.087640
## iter 20 value 18.069332
## final value 18.069329
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0151 1.98 -2.11 0.0352
## 2 decrease similarity 0.605 0.297 1.43 0.152
## 3 increase (Intercept) 0.880 3.88 0.514 0.607
## 4 increase similarity 0.205 1.20 -1.13 0.257tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 17.635355
## iter 20 value 17.528445
## iter 30 value 17.486656
## iter 40 value 17.448163
## iter 50 value 17.437145
## iter 60 value 17.422688
## iter 70 value 17.420293
## iter 80 value 17.416758
## iter 90 value 17.415277
## iter 100 value 17.412877
## final value 17.412877
## stopped after 100 iterations## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.112 2.35 -0.883 0.377
## 2 decrease likeability 0.518 0.330 0.219 0.827
## 3 increase (Intercept) 1.00 32.5 0.743 0.458
## 4 increase likeability 0.00244 8.06 -0.746 0.456tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 18.778922
## final value 18.770108
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0394 1.96 -1.63 0.103
## 2 decrease becomeFriends 0.565 0.298 0.878 0.380
## 3 increase (Intercept) 0.825 3.40 0.457 0.648
## 4 increase becomeFriends 0.236 1.04 -1.13 0.257No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 20.228362
## iter 20 value 20.201092
## final value 20.200886
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0234 2.70 -1.38 0.168
## 2 decrease extraversion 0.648 0.740 0.825 0.409
## 3 increase (Intercept) 0.153 5.97 -0.286 0.775
## 4 increase extraversion 0.378 1.81 -0.275 0.783tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 17.297557
## iter 20 value 16.255114
## iter 30 value 16.081367
## iter 40 value 16.053187
## iter 50 value 16.001202
## iter 60 value 16.000199
## iter 70 value 15.998703
## iter 80 value 15.998309
## iter 90 value 15.997488
## iter 100 value 15.997023
## final value 15.997023
## stopped after 100 iterations## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 8.70e- 1 2.08 0.913 0.361
## 2 decrease openness 2.65e- 1 0.628 -1.62 0.104
## 3 increase (Intercept) 1 e+ 0 73.6 0.702 0.483
## 4 increase openness 8.06e-12 36.6 -0.697 0.486tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 18.994697
## iter 20 value 18.822553
## iter 30 value 18.802293
## final value 18.802293
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00192 3.61 -1.73 0.0834
## 2 decrease agreeableness 0.789 0.986 1.34 0.180
## 3 increase (Intercept) 0.999 10.1 0.706 0.480
## 4 increase agreeableness 0.0318 3.51 -0.974 0.330tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 17.722737
## iter 20 value 17.357139
## iter 30 value 17.339553
## iter 40 value 17.339357
## iter 50 value 17.339324
## final value 17.339321
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.000296 4.81 -1.69 0.0914
## 2 decrease conscientiousness 0.827 1.11 1.40 0.160
## 3 increase (Intercept) 1.00 8.45 1.09 0.277
## 4 increase conscientiousness 0.0221 2.96 -1.28 0.200tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 39.550042
## iter 10 value 19.998733
## iter 20 value 19.988628
## final value 19.988549
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.618 1.92 0.250 0.803
## 2 decrease neuroticism 0.338 0.634 -1.06 0.288
## 3 increase (Intercept) 0.119 4.32 -0.465 0.642
## 4 increase neuroticism 0.393 1.40 -0.313 0.754No effect of forehead temperature change on f0 SD change
ggplot(ts |> filter(section=="Diapix", feature=="f0SD"), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 18.005433
## iter 20 value 17.969731
## iter 30 value 17.964401
## iter 30 value 17.964401
## iter 30 value 17.964401
## final value 17.964401
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0588 1.03 -2.69 0.00715
## 2 f0Decrease ForeheadtempDecrease 0.00183 93.3 -0.0675 0.946
## 3 f0Decrease ForeheadtempIncrease 0.870 1.16 1.64 0.102
## 4 f0Increase (Intercept) 0.0000278 47.4 -0.221 0.825
## 5 f0Increase ForeheadtempDecrease 0.300 294. -0.00289 0.998
## 6 f0Increase ForeheadtempIncrease 1.00 47.4 0.169 0.866emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 9.41e-01 0.05708 6 0.80147 1.08082
## tempDecrease 1.00e+00 0.01126 6 0.97233 1.02741
## tempIncrease 6.67e-01 0.11111 6 0.39479 0.93855
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 5.88e-02 0.05707 6 -0.08082 0.19847
## tempDecrease 1.15e-04 0.01071 6 -0.02610 0.02633
## tempIncrease 2.78e-01 0.10557 6 0.01946 0.53611
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 2.62e-05 0.00124 6 -0.00301 0.00306
## tempDecrease 1.19e-05 0.00345 6 -0.00844 0.00846
## tempIncrease 5.55e-02 0.05399 6 -0.07655 0.18764
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -5.87e-02 0.05818 6 -1.009 0.5982
## tempNS - tempIncrease 2.74e-01 0.12492 6 2.197 0.1502
## tempDecrease - tempIncrease 3.33e-01 0.11168 6 2.984 0.0555
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 5.87e-02 0.05807 6 1.011 0.5972
## tempNS - tempIncrease -2.19e-01 0.12001 6 -1.824 0.2404
## tempDecrease - tempIncrease -2.78e-01 0.10611 6 -2.617 0.0880
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.43e-05 0.00367 6 0.004 1.0000
## tempNS - tempIncrease -5.55e-02 0.05400 6 -1.028 0.5878
## tempDecrease - tempIncrease -5.55e-02 0.05410 6 -1.027 0.5887
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 SD change
ggplot(ts |> filter(section=="Diapix", feature=="f0SD"), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 16.659205
## iter 20 value 16.633363
## final value 16.631764
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0000119 91.8 -0.124 0.902
## 2 f0Decrease NosetempDecrease 0.0236 937. -0.00397 0.997
## 3 f0Decrease NosetempIncrease 1.00 91.8 0.114 0.910
## 4 f0Increase (Intercept) 0.00000192 228. -0.0577 0.954
## 5 f0Increase NosetempDecrease 0.0601 1446. -0.00190 0.998
## 6 f0Increase NosetempIncrease 1.00 228. 0.0458 0.963emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.9999862 0.0011745 6 0.9971123 1.0028601
## tempDecrease 0.9999996 0.0003202 6 0.9992161 1.0007831
## tempIncrease 0.6818308 0.0993016 6 0.4388486 0.9248129
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.0000119 0.0010898 6 -0.0026547 0.0026785
## tempDecrease 0.0000003 0.0002681 6 -0.0006557 0.0006563
## tempIncrease 0.2727315 0.0949519 6 0.0403926 0.5050705
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 0.0000019 0.0004380 6 -0.0010699 0.0010737
## tempDecrease 0.0000001 0.0001751 6 -0.0004283 0.0004285
## tempIncrease 0.0454377 0.0444016 6 -0.0632092 0.1540846
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -1.34e-05 0.001217 6 -0.011 0.9999
## tempNS - tempIncrease 3.18e-01 0.099308 6 3.204 0.0424
## tempDecrease - tempIncrease 3.18e-01 0.099302 6 3.204 0.0423
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.16e-05 0.001122 6 0.010 0.9999
## tempNS - tempIncrease -2.73e-01 0.094958 6 -2.872 0.0638
## tempDecrease - tempIncrease -2.73e-01 0.094952 6 -2.872 0.0638
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.80e-06 0.000472 6 0.004 1.0000
## tempNS - tempIncrease -4.54e-02 0.044404 6 -1.023 0.5905
## tempDecrease - tempIncrease -4.54e-02 0.044402 6 -1.023 0.5905
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 SD change
ggplot(ts |> filter(section=="Diapix", feature=="f0SD", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0SD", section=="Diapix", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 31.859756
## iter 10 value 16.386908
## iter 20 value 16.357005
## iter 30 value 16.355091
## iter 30 value 16.355091
## iter 30 value 16.355090
## final value 16.355090
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.167 0.775 -2.08 0.0377
## 2 f0Decrease EyestempDecrease 0.0000562 211. -0.0464 0.963
## 3 f0Decrease EyestempIncrease 0.577 1.01 0.306 0.759
## 4 f0Increase (Intercept) 0.0000258 62.3 -0.170 0.865
## 5 f0Increase EyestempDecrease 0.130 366. -0.00520 0.996
## 6 f0Increase EyestempIncrease 1.00 62.3 0.131 0.896emmeans(m, pairwise~Eyes, by="change") # isn't working (I'm thinking maybe because "eyes" have lots of missing values)## $emmeans
## change = f0NS:
## Eyes prob SE df lower.CL upper.CL
## tempNS 8.33e-01 0.10759 6 0.57006 1.09657
## tempDecrease 1.00e+00 0.00275 6 0.99326 1.00671
## tempIncrease 7.33e-01 0.11418 6 0.45396 1.01273
##
## change = f0Decrease:
## Eyes prob SE df lower.CL upper.CL
## tempNS 1.67e-01 0.10758 6 -0.09658 0.42991
## tempDecrease 1.12e-05 0.00237 6 -0.00579 0.00581
## tempIncrease 2.00e-01 0.10328 6 -0.05272 0.45271
##
## change = f0Increase:
## Eyes prob SE df lower.CL upper.CL
## tempNS 2.15e-05 0.00134 6 -0.00325 0.00329
## tempDecrease 3.90e-06 0.00139 6 -0.00339 0.00340
## tempIncrease 6.67e-02 0.06440 6 -0.09093 0.22424
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -1.67e-01 0.10762 6 -1.549 0.3358
## tempNS - tempIncrease 1.00e-01 0.15688 6 0.637 0.8060
## tempDecrease - tempIncrease 2.67e-01 0.11421 6 2.335 0.1261
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.67e-01 0.10761 6 1.549 0.3358
## tempNS - tempIncrease -3.33e-02 0.14913 6 -0.224 0.9730
## tempDecrease - tempIncrease -2.00e-01 0.10331 6 -1.936 0.2092
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 1.76e-05 0.00193 6 0.009 1.0000
## tempNS - tempIncrease -6.66e-02 0.06442 6 -1.034 0.5843
## tempDecrease - tempIncrease -6.67e-02 0.06442 6 -1.035 0.5841
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of cheek temperature change on f0 SD change
ggplot(ts |> filter(section=="Diapix", feature=="f0SD"), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0SD", section=="Diapix"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 39.550042
## iter 10 value 17.591246
## iter 20 value 17.569183
## final value 17.567114
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.278 0.526 -1.82 0.0694
## 2 f0Decrease CheekstempDecrease 0.0000123 154. -0.0736 0.941
## 3 f0Decrease CheekstempIncrease 0.271 1.19 -0.831 0.406
## 4 f0Increase (Intercept) 0.0714 1.04 -2.47 0.0134
## 5 f0Increase CheekstempDecrease 0.0000958 123. -0.0753 0.940
## 6 f0Increase CheekstempIncrease 0.0000642 170. -0.0567 0.955emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.6842184 0.1066385 6 0.423283 0.945153
## tempDecrease 0.9999879 0.0011585 6 0.997153 1.002823
## tempIncrease 0.8749940 0.1169292 6 0.588879 1.161109
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.2631532 0.1010220 6 0.015961 0.510345
## tempDecrease 0.0000047 0.0007235 6 -0.001766 0.001775
## tempIncrease 0.1250017 0.1169275 6 -0.161109 0.411113
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 0.0526283 0.0512263 6 -0.072718 0.177975
## tempDecrease 0.0000074 0.0009047 6 -0.002206 0.002221
## tempIncrease 0.0000043 0.0007348 6 -0.001794 0.001802
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -3.16e-01 0.10664 6 -2.961 0.0571
## tempNS - tempIncrease -1.91e-01 0.15825 6 -1.206 0.4926
## tempDecrease - tempIncrease 1.25e-01 0.11693 6 1.069 0.5653
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 2.63e-01 0.10102 6 2.605 0.0893
## tempNS - tempIncrease 1.38e-01 0.15452 6 0.894 0.6634
## tempDecrease - tempIncrease -1.25e-01 0.11693 6 -1.069 0.5652
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 5.26e-02 0.05123 6 1.027 0.5884
## tempNS - tempIncrease 5.26e-02 0.05123 6 1.027 0.5883
## tempDecrease - tempIncrease 3.05e-06 0.00117 6 0.003 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesAcross entire experiment
f0 median
No effect of condition
ggplot(dat |> filter(feature=="f0median", section=="entireExp"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 median across entire experiment")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.045493
## iter 20 value 29.026327
## final value 29.026326
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0000151 68.9 -0.161 0.872
## 2 decrease conditionimpersonal 1.00 68.9 0.142 0.887
## 3 increase (Intercept) 0.300 0.488 -1.74 0.0825
## 4 increase conditionimpersonal 0.459 0.761 -0.216 0.829emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 7.00e-01 0.102470 4 0.41550 0.98450
## impersonal 6.11e-01 0.114905 4 0.29207 0.93013
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 1.05e-05 0.000726 4 -0.00201 0.00203
## impersonal 1.67e-01 0.087841 4 -0.07722 0.41056
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 3.00e-01 0.102469 4 0.01549 0.58449
## impersonal 2.22e-01 0.097993 4 -0.04984 0.49431
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.0889 0.1540 4 0.577 0.5946
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.1667 0.0878 4 -1.897 0.1307
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.0778 0.1418 4 0.548 0.6126No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 31.209929
## final value 31.209712
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.138 1.31 -1.40 0.162
## 2 decrease closeness 0.479 0.345 -0.239 0.811
## 3 increase (Intercept) 0.205 0.865 -1.56 0.118
## 4 increase closeness 0.529 0.201 0.574 0.566tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 31.340790
## iter 10 value 31.340790
## final value 31.340790
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0570 2.20 -1.28 0.202
## 2 decrease similarity 0.529 0.354 0.333 0.739
## 3 increase (Intercept) 0.339 1.23 -0.543 0.587
## 4 increase similarity 0.489 0.212 -0.211 0.833tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 31.409907
## final value 31.409844
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0737 3.08 -0.822 0.411
## 2 decrease likeability 0.515 0.432 0.137 0.891
## 3 increase (Intercept) 0.343 1.81 -0.360 0.719
## 4 increase likeability 0.490 0.260 -0.149 0.882tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 31.214880
## final value 31.214880
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.300 1.93 -0.440 0.660
## 2 decrease becomeFriends 0.444 0.338 -0.661 0.509
## 3 increase (Intercept) 0.347 1.34 -0.471 0.638
## 4 increase becomeFriends 0.488 0.217 -0.220 0.826No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.975297
## iter 20 value 29.848747
## iter 20 value 29.848747
## iter 20 value 29.848746
## final value 29.848746
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.00374 3.90 -1.43 0.152
## 2 decrease extraversion 0.729 1.06 0.933 0.351
## 3 increase (Intercept) 0.00872 2.46 -1.93 0.0539
## 4 increase extraversion 0.748 0.677 1.60 0.109tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 31.249348
## iter 20 value 31.240598
## iter 20 value 31.240598
## iter 20 value 31.240598
## final value 31.240598
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.171 2.63 -0.599 0.549
## 2 decrease openness 0.461 0.753 -0.210 0.833
## 3 increase (Intercept) 0.140 1.74 -1.05 0.295
## 4 increase openness 0.563 0.470 0.537 0.591tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 30.317732
## iter 20 value 30.175384
## iter 20 value 30.175384
## final value 30.175384
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.000395 4.87 -1.61 0.108
## 2 decrease agreeableness 0.832 1.30 1.23 0.218
## 3 increase (Intercept) 0.0186 2.74 -1.45 0.148
## 4 increase agreeableness 0.706 0.771 1.14 0.255tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 30.279952
## iter 20 value 30.188601
## final value 30.188541
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0000121 7.59 -1.49 0.136
## 2 decrease conscientiousness 0.897 1.71 1.26 0.207
## 3 increase (Intercept) 0.487 2.60 -0.0198 0.984
## 4 increase conscientiousness 0.446 0.650 -0.335 0.738tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.638191
## final value 29.629303
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.808 2.72 0.528 0.597
## 2 decrease neuroticism 0.236 0.936 -1.25 0.210
## 3 increase (Intercept) 0.858 1.77 1.02 0.309
## 4 increase neuroticism 0.294 0.573 -1.53 0.125No effect of forehead temperature change on f0 median change
ggplot(ts |> filter(section=="entireExp", feature=="f0median"), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 29.919868
## iter 20 value 29.859819
## iter 30 value 29.855467
## iter 30 value 29.855467
## iter 30 value 29.855467
## final value 29.855467
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.111 1.06 -1.96 0.0499
## 2 f0Decrease ForeheadtempDecrease 0.00144 74.6 -0.0877 0.930
## 3 f0Decrease ForeheadtempIncrease 0.500 1.30 0.000206 1.00
## 4 f0Increase (Intercept) 0.111 1.06 -1.96 0.0499
## 5 f0Increase ForeheadtempDecrease 0.000863 96.3 -0.0733 0.942
## 6 f0Increase ForeheadtempIncrease 0.818 1.14 1.32 0.187emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.800035 0.1265 6 0.4905 1.1095
## tempDecrease 0.999712 0.0170 6 0.9582 1.0412
## tempIncrease 0.592590 0.0946 6 0.3612 0.8240
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.099979 0.0949 6 -0.1321 0.3321
## tempDecrease 0.000180 0.0134 6 -0.0326 0.0330
## tempIncrease 0.074075 0.0504 6 -0.0493 0.1974
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.099987 0.0949 6 -0.1321 0.3321
## tempDecrease 0.000108 0.0104 6 -0.0253 0.0255
## tempIncrease 0.333335 0.0907 6 0.1113 0.5553
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.1997 0.1276 6 -1.565 0.3295
## tempNS - tempIncrease 0.2074 0.1579 6 1.314 0.4388
## tempDecrease - tempIncrease 0.4071 0.0961 6 4.238 0.0129
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0998 0.0958 6 1.042 0.5802
## tempNS - tempIncrease 0.0259 0.1074 6 0.241 0.9686
## tempDecrease - tempIncrease -0.0739 0.0522 6 -1.417 0.3911
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0999 0.0954 6 1.047 0.5775
## tempNS - tempIncrease -0.2333 0.1313 6 -1.778 0.2547
## tempDecrease - tempIncrease -0.3332 0.0913 6 -3.649 0.0249
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 median change
ggplot(ts |> filter(section=="entireExp", feature=="f0median"), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 30.047237
## iter 20 value 29.977360
## iter 30 value 29.972833
## final value 29.972828
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0000233 147. -0.0728 0.942
## 2 f0Decrease NosetempDecrease 0.00952 1230. -0.00378 0.997
## 3 f0Decrease NosetempIncrease 1.00 147. 0.0598 0.952
## 4 f0Increase (Intercept) 0.0000670 86.4 -0.111 0.911
## 5 f0Increase NosetempDecrease 1.00 86.4 0.107 0.915
## 6 f0Increase NosetempIncrease 1.00 86.4 0.101 0.920emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 1.00e+00 0.006716 6 0.983477 1.016343
## tempDecrease 6.00e-01 0.219092 6 0.063866 1.136065
## tempIncrease 6.45e-01 0.085935 6 0.434878 0.855429
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 2.33e-05 0.003410 6 -0.008322 0.008368
## tempDecrease 1.00e-07 0.000164 6 -0.000401 0.000401
## tempIncrease 9.68e-02 0.053101 6 -0.033157 0.226707
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 6.70e-05 0.005786 6 -0.014090 0.014224
## tempDecrease 4.00e-01 0.219092 6 -0.136065 0.936134
## tempIncrease 2.58e-01 0.078591 6 0.065767 0.450376
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 4.00e-01 0.21920 6 1.825 0.2403
## tempNS - tempIncrease 3.55e-01 0.08620 6 4.116 0.0147
## tempDecrease - tempIncrease -4.52e-02 0.23534 6 -0.192 0.9799
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 2.31e-05 0.00341 6 0.007 1.0000
## tempNS - tempIncrease -9.68e-02 0.05321 6 -1.818 0.2422
## tempDecrease - tempIncrease -9.68e-02 0.05310 6 -1.822 0.2410
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -4.00e-01 0.21917 6 -1.825 0.2402
## tempNS - tempIncrease -2.58e-01 0.07880 6 -3.274 0.0389
## tempDecrease - tempIncrease 1.42e-01 0.23276 6 0.610 0.8202
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 median change
ggplot(ts |> filter(section=="entireExp", feature=="f0median", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 median change")
The model is weirdly producing NaNs for the category
tempDecrease even when I’ve explicitly removed the NA
values in the dataset (and also after droplevel-ing it).
But anyway, there are such few values in the tempNS
category that a real comparison wouldn’t have been meaningful, like for
other models above.
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0median", section=="entireExp", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 36.254206
## iter 10 value 21.083942
## iter 20 value 21.050606
## iter 30 value 21.048274
## iter 30 value 21.048274
## iter 30 value 21.048274
## final value 21.048274
## converged## Warning in sqrt(diag(vc)): NaNs produced## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0000161 102. -0.108 0.914
## 2 f0Decrease EyestempDecrease 0.5 NaN NaN NaN
## 3 f0Decrease EyestempIncrease 1.00 102. 0.0806 0.936
## 4 f0Increase (Intercept) 0.0000384 65.9 -0.154 0.877
## 5 f0Increase EyestempDecrease 0.5 0 NaN NaN
## 6 f0Increase EyestempIncrease 1.00 65.9 0.145 0.885# emmeans(m, pairwise~Eyes, by="change")No effect of cheek temperature change on f0 median change
ggplot(ts |> filter(section=="entireExp", feature=="f0median") |> drop_na(), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 median change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0median", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 30.209177
## iter 20 value 30.194803
## final value 30.193839
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.182 0.782 -1.92 0.0543
## 2 f0Decrease CheekstempDecrease 0.0000545 109. -0.0903 0.928
## 3 f0Decrease CheekstempIncrease 0.333 1.31 -0.528 0.597
## 4 f0Increase (Intercept) 0.357 0.558 -1.05 0.292
## 5 f0Increase CheekstempDecrease 0.340 0.977 -0.681 0.496
## 6 f0Increase CheekstempIncrease 0.375 0.869 -0.588 0.557emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 5.63e-01 0.12402 6 0.25906 0.86599
## tempDecrease 7.78e-01 0.13858 6 0.43867 1.11687
## tempIncrease 6.92e-01 0.12800 6 0.37915 1.00557
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 1.25e-01 0.08267 6 -0.07733 0.32723
## tempDecrease 9.40e-06 0.00102 6 -0.00249 0.00251
## tempIncrease 7.68e-02 0.07387 6 -0.10391 0.25761
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 3.13e-01 0.11588 6 0.02897 0.59607
## tempDecrease 2.22e-01 0.13858 6 -0.11687 0.56131
## tempIncrease 2.31e-01 0.11686 6 -0.05515 0.51674
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.21524 0.1860 6 -1.157 0.5176
## tempNS - tempIncrease -0.12983 0.1782 6 -0.728 0.7567
## tempDecrease - tempIncrease 0.08541 0.1887 6 0.453 0.8951
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.12494 0.0827 6 1.511 0.3508
## tempNS - tempIncrease 0.04810 0.1109 6 0.434 0.9030
## tempDecrease - tempIncrease -0.07684 0.0739 6 -1.040 0.5812
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.09030 0.1806 6 0.500 0.8741
## tempNS - tempIncrease 0.08173 0.1646 6 0.497 0.8756
## tempDecrease - tempIncrease -0.00857 0.1813 6 -0.047 0.9988
##
## P value adjustment: tukey method for comparing a family of 3 estimatesf0 max
No effect of condition
ggplot(dat |> filter(feature=="f0max", section=="entireExp"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 max across entire experiment")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 28.079314
## iter 20 value 28.065243
## final value 28.065230
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.389 0.483 -0.935 0.350
## 2 decrease conditionimpersonal 0.310 0.745 -1.07 0.282
## 3 increase (Intercept) 0.154 0.769 -2.22 0.0266
## 4 increase conditionimpersonal 0.000152 50.9 -0.173 0.863emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 5.50e-01 0.11124 4 0.24115 0.85887
## impersonal 7.78e-01 0.09799 4 0.50570 1.04984
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 3.50e-01 0.10665 4 0.05389 0.64612
## impersonal 2.22e-01 0.09799 4 -0.04985 0.49427
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 1.00e-01 0.06708 4 -0.08625 0.28622
## impersonal 2.15e-05 0.00109 4 -0.00301 0.00305
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.228 0.1482 4 -1.536 0.1993
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.128 0.1448 4 0.882 0.4274
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.100 0.0671 4 1.490 0.2105No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.859869
## final value 29.848255
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.390 0.798 -0.563 0.574
## 2 decrease closeness 0.474 0.200 -0.514 0.607
## 3 increase (Intercept) 0.0996 1.61 -1.37 0.170
## 4 increase closeness 0.478 0.406 -0.219 0.827tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.849406
## final value 29.849368
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.273 1.23 -0.800 0.424
## 2 decrease similarity 0.507 0.206 0.137 0.891
## 3 increase (Intercept) 0.193 2.26 -0.635 0.526
## 4 increase similarity 0.449 0.422 -0.487 0.626tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.953332
## iter 20 value 29.909757
## iter 20 value 29.909757
## final value 29.909756
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.183 1.81 -0.828 0.408
## 2 decrease likeability 0.524 0.255 0.383 0.701
## 3 increase (Intercept) 0.0388 3.71 -0.865 0.387
## 4 increase likeability 0.525 0.520 0.190 0.849tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.785106
## final value 29.784995
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.427 1.26 -0.235 0.814
## 2 decrease becomeFriends 0.478 0.207 -0.433 0.665
## 3 increase (Intercept) 0.0241 3.15 -1.18 0.240
## 4 increase becomeFriends 0.547 0.473 0.396 0.692No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.147155
## iter 20 value 29.061470
## iter 20 value 29.061470
## iter 20 value 29.061470
## final value 29.061470
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.662 2.19 0.307 0.759
## 2 decrease extraversion 0.392 0.639 -0.686 0.493
## 3 increase (Intercept) 0.000508 5.32 -1.42 0.154
## 4 increase extraversion 0.796 1.35 1.01 0.313tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.918257
## iter 20 value 29.909895
## iter 20 value 29.909895
## iter 20 value 29.909895
## final value 29.909895
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.332 1.61 -0.437 0.662
## 2 decrease openness 0.491 0.446 -0.0765 0.939
## 3 increase (Intercept) 0.0215 3.58 -1.07 0.286
## 4 increase openness 0.588 0.937 0.379 0.704tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.718774
## final value 29.689323
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.187 2.51 -0.585 0.559
## 2 decrease agreeableness 0.547 0.717 0.261 0.794
## 3 increase (Intercept) 0.00147 5.55 -1.18 0.240
## 4 increase agreeableness 0.754 1.49 0.751 0.453tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.780386
## iter 20 value 29.761308
## final value 29.761107
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0815 2.78 -0.872 0.383
## 2 decrease conscientiousness 0.598 0.676 0.585 0.559
## 3 increase (Intercept) 0.202 4.77 -0.288 0.773
## 4 increase conscientiousness 0.427 1.21 -0.242 0.809tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 29.598303
## final value 29.595088
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.569 1.62 0.170 0.865
## 2 decrease neuroticism 0.413 0.510 -0.686 0.493
## 3 increase (Intercept) 0.393 3.13 -0.139 0.890
## 4 increase neuroticism 0.335 1.05 -0.655 0.513No effect of forehead temperature change on f0 max change
ggplot(ts |> filter(section=="entireExp", feature=="f0max"), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 29.137489
## iter 20 value 29.070581
## iter 30 value 29.066494
## iter 30 value 29.066494
## iter 30 value 29.066494
## final value 29.066494
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.222 0.802 -1.56 0.118
## 2 f0Decrease ForeheadtempDecrease 0.000901 62.3 -0.112 0.910
## 3 f0Decrease ForeheadtempIncrease 0.650 0.902 0.684 0.494
## 4 f0Increase (Intercept) 0.125 1.07 -1.82 0.0687
## 5 f0Increase ForeheadtempDecrease 0.000721 98.6 -0.0734 0.941
## 6 f0Increase ForeheadtempIncrease 0.292 1.48 -0.598 0.550emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.700027 0.1449 6 0.3454 1.0546
## tempDecrease 0.999640 0.0190 6 0.9532 1.0461
## tempIncrease 0.629618 0.0929 6 0.4022 0.8570
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.200006 0.1265 6 -0.1095 0.5095
## tempDecrease 0.000258 0.0160 6 -0.0390 0.0395
## tempIncrease 0.333353 0.0907 6 0.1114 0.5553
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 0.099968 0.0949 6 -0.1321 0.3321
## tempDecrease 0.000103 0.0101 6 -0.0247 0.0249
## tempIncrease 0.037029 0.0363 6 -0.0519 0.1260
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.2996 0.1461 6 -2.050 0.1811
## tempNS - tempIncrease 0.0704 0.1722 6 0.409 0.9132
## tempDecrease - tempIncrease 0.3700 0.0949 6 3.901 0.0187
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.1997 0.1275 6 1.567 0.3287
## tempNS - tempIncrease -0.1333 0.1557 6 -0.857 0.6846
## tempDecrease - tempIncrease -0.3331 0.0921 6 -3.615 0.0259
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0999 0.0954 6 1.047 0.5774
## tempNS - tempIncrease 0.0629 0.1016 6 0.620 0.8152
## tempDecrease - tempIncrease -0.0369 0.0377 6 -0.979 0.6155
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 max change
ggplot(ts |> filter(section=="entireExp", feature=="f0max"), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 26.766378
## iter 20 value 26.643122
## iter 30 value 26.634308
## iter 40 value 26.633175
## iter 40 value 26.633175
## final value 26.633175
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.999 29.1 0.232 0.817
## 2 f0Decrease NosetempDecrease 0.000296 29.1 -0.279 0.780
## 3 f0Decrease NosetempIncrease 0.000508 29.1 -0.261 0.794
## 4 f0Increase (Intercept) 0.999 29.1 0.232 0.817
## 5 f0Increase NosetempDecrease 0.0000000210 122. -0.144 0.885
## 6 f0Increase NosetempIncrease 0.0000566 29.1 -0.336 0.737emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 5.93e-04 0.01721 6 -0.0415 0.04271
## tempDecrease 8.00e-01 0.17888 6 0.3623 1.23772
## tempIncrease 6.77e-01 0.08396 6 0.4719 0.88282
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 5.00e-01 0.35355 6 -0.3650 1.36519
## tempDecrease 2.00e-01 0.17888 6 -0.2377 0.63768
## tempIncrease 2.90e-01 0.08153 6 0.0909 0.48984
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 4.99e-01 0.35355 6 -0.3658 1.36445
## tempDecrease 1.41e-05 0.00168 6 -0.0041 0.00413
## tempIncrease 3.23e-02 0.03174 6 -0.0454 0.10994
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.7994 0.1797 6 -4.448 0.0103
## tempNS - tempIncrease -0.6768 0.0857 6 -7.896 0.0005
## tempDecrease - tempIncrease 0.1226 0.1976 6 0.621 0.8147
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.3001 0.3962 6 0.757 0.7406
## tempNS - tempIncrease 0.2097 0.3628 6 0.578 0.8364
## tempDecrease - tempIncrease -0.0904 0.1966 6 -0.460 0.8920
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.4993 0.3536 6 1.412 0.3931
## tempNS - tempIncrease 0.4671 0.3550 6 1.316 0.4378
## tempDecrease - tempIncrease -0.0323 0.0318 6 -1.015 0.5951
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 max change (but again, model not working even after filtering out NAs…)
ggplot(ts |> filter(section=="entireExp", feature=="f0max", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0max", section=="entireExp", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 36.254206
## iter 10 value 25.524201
## final value 25.505825
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.167 1.10e+ 0 -1.47 0.142
## 2 f0Decrease EyestempDecrease 0.5 1.05e-11 0 1
## 3 f0Decrease EyestempIncrease 0.702 1.18e+ 0 0.727 0.467
## 4 f0Increase (Intercept) 0.000263 2.76e+ 1 -0.299 0.765
## 5 f0Increase EyestempDecrease 0.5 0 NaN NaN
## 6 f0Increase EyestempIncrease 0.998 2.76e+ 1 0.221 0.825# emmeans(m, pairwise~Eyes, by="change")No effect of cheek temperature change on f0 max change
ggplot(ts |> filter(section=="entireExp", feature=="f0max"), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 max change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0max", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 27.982898
## iter 20 value 27.946866
## iter 30 value 27.944554
## final value 27.944553
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.357 0.558 -1.05 0.292
## 2 f0Decrease CheekstempDecrease 0.340 0.977 -0.681 0.496
## 3 f0Decrease CheekstempIncrease 0.444 0.820 -0.272 0.786
## 4 f0Increase (Intercept) 0.182 0.782 -1.92 0.0543
## 5 f0Increase CheekstempDecrease 0.0000745 92.9 -0.102 0.919
## 6 f0Increase CheekstempIncrease 0.0000119 205. -0.0553 0.956emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 5.63e-01 0.124019 6 0.259045 0.865973
## tempDecrease 7.78e-01 0.138580 6 0.438687 1.116871
## tempIncrease 6.92e-01 0.128008 6 0.379076 1.005527
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 3.13e-01 0.115879 6 0.028962 0.596053
## tempDecrease 2.22e-01 0.138577 6 -0.116877 0.561293
## tempIncrease 3.08e-01 0.128008 6 -0.005528 0.620922
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 1.25e-01 0.082675 6 -0.077315 0.327282
## tempDecrease 1.29e-05 0.001196 6 -0.002914 0.002940
## tempIncrease 1.80e-06 0.000375 6 -0.000916 0.000919
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.215270 0.18597 6 -1.158 0.5175
## tempNS - tempIncrease -0.129792 0.17823 6 -0.728 0.7568
## tempDecrease - tempIncrease 0.085478 0.18865 6 0.453 0.8949
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.090300 0.18064 6 0.500 0.8741
## tempNS - tempIncrease 0.004810 0.17267 6 0.028 0.9996
## tempDecrease - tempIncrease -0.085489 0.18865 6 -0.453 0.8949
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.124971 0.08268 6 1.511 0.3507
## tempNS - tempIncrease 0.124982 0.08268 6 1.512 0.3506
## tempDecrease - tempIncrease 0.000011 0.00125 6 0.009 1.0000
##
## P value adjustment: tukey method for comparing a family of 3 estimatesf0 SD
No effect of condition
ggplot(dat |> filter(feature=="f0SD", section=="entireExp"), aes(condition, fill=change))+
geom_bar()+
scale_fill_manual(values = c("decrease" = "lightblue", "increase" = "red", "ns" = "gray"))+
ggtitle("f0 SD across entire experiment")
tidy(m <- nnet::multinom(change ~ condition, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 9 (4 variable)
## initial value 41.747267
## final value 32.831139
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0667 1.04 -2.55 0.0108
## 2 decrease conditionimpersonal 0.848 1.19 1.44 0.148
## 3 increase (Intercept) 0.263 0.521 -1.98 0.0481
## 4 increase conditionimpersonal 0.528 0.788 0.144 0.886emmeans(m, pairwise~condition, by="change")## $emmeans
## change = ns:
## condition prob SE df lower.CL upper.CL
## close 0.700 0.1025 4 0.4155 0.985
## impersonal 0.556 0.1171 4 0.2304 0.881
##
## change = decrease:
## condition prob SE df lower.CL upper.CL
## close 0.050 0.0487 4 -0.0853 0.185
## impersonal 0.222 0.0980 4 -0.0498 0.494
##
## change = increase:
## condition prob SE df lower.CL upper.CL
## close 0.250 0.0968 4 -0.0188 0.519
## impersonal 0.222 0.0980 4 -0.0498 0.494
##
## Confidence level used: 0.95
##
## $contrasts
## change = ns:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.1444 0.156 4 0.928 0.4058
##
## change = decrease:
## contrast estimate SE df t.ratio p.value
## close - impersonal -0.1722 0.109 4 -1.574 0.1907
##
## change = increase:
## contrast estimate SE df t.ratio p.value
## close - impersonal 0.0278 0.138 4 0.202 0.8500No effect of perception of partner
tidy(m <- nnet::multinom(change ~ closeness, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## final value 33.927892
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.273 1.05 -0.929 0.353
## 2 decrease closeness 0.458 0.283 -0.602 0.547
## 3 increase (Intercept) 0.267 0.883 -1.14 0.252
## 4 increase closeness 0.502 0.210 0.0382 0.969tidy(m <- nnet::multinom(change ~ similarity, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 33.073731
## iter 10 value 33.073731
## final value 33.073731
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.194 1.72 -0.825 0.410
## 2 decrease similarity 0.494 0.284 -0.0884 0.930
## 3 increase (Intercept) 0.685 1.28 0.607 0.544
## 4 increase similarity 0.419 0.235 -1.39 0.164tidy(m <- nnet::multinom(change ~ likeability, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 33.604388
## final value 33.604384
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.623 2.30 0.219 0.826
## 2 decrease likeability 0.424 0.344 -0.896 0.370
## 3 increase (Intercept) 0.573 1.90 0.156 0.876
## 4 increase likeability 0.454 0.274 -0.679 0.497tidy(m <- nnet::multinom(change ~ becomeFriends, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 33.913787
## iter 10 value 33.913787
## final value 33.913787
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.272 1.63 -0.603 0.546
## 2 decrease becomeFriends 0.475 0.275 -0.368 0.713
## 3 increase (Intercept) 0.164 1.50 -1.09 0.277
## 4 increase becomeFriends 0.527 0.235 0.455 0.649No effect of BFI scores
tidy(m <- nnet::multinom(change ~ extraversion, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 34.018733
## final value 34.017565
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.186 2.92 -0.506 0.613
## 2 decrease extraversion 0.493 0.828 -0.0319 0.975
## 3 increase (Intercept) 0.526 2.32 0.0449 0.964
## 4 increase extraversion 0.422 0.672 -0.471 0.638tidy(m <- nnet::multinom(change ~ openness, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 33.522097
## final value 33.521832
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0282 2.42 -1.46 0.143
## 2 decrease openness 0.634 0.638 0.860 0.390
## 3 increase (Intercept) 0.0791 1.84 -1.33 0.183
## 4 increase openness 0.602 0.499 0.832 0.405tidy(m <- nnet::multinom(change ~ agreeableness, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 33.862243
## final value 33.860466
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.185 3.42 -0.434 0.664
## 2 decrease agreeableness 0.494 0.970 -0.0252 0.980
## 3 increase (Intercept) 0.724 2.72 0.355 0.723
## 4 increase agreeableness 0.362 0.794 -0.716 0.474tidy(m <- nnet::multinom(change ~ conscientiousness, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 33.930485
## final value 33.928000
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.194 3.62 -0.393 0.695
## 2 decrease conscientiousness 0.491 0.887 -0.0405 0.968
## 3 increase (Intercept) 0.672 2.69 0.267 0.789
## 4 increase conscientiousness 0.395 0.671 -0.634 0.526tidy(m <- nnet::multinom(change ~ neuroticism, data=dat |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 41.747267
## iter 10 value 34.071871
## final value 34.071086
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.202 2.21 -0.621 0.534
## 2 decrease neuroticism 0.484 0.682 -0.0912 0.927
## 3 increase (Intercept) 0.405 1.73 -0.222 0.825
## 4 increase neuroticism 0.453 0.543 -0.350 0.726No effect of forehead temperature change on f0 SD change
ggplot(ts |> filter(section=="entireExp", feature=="f0SD"), aes(Forehead, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Forehead temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Forehead, data=ts |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 32.559031
## iter 20 value 32.546476
## iter 30 value 32.545534
## final value 32.545532
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.125 1.07 -1.82 0.0687
## 2 f0Decrease ForeheadtempDecrease 0.235 470. -0.00251 0.998
## 3 f0Decrease ForeheadtempIncrease 0.622 1.20 0.414 0.679
## 4 f0Increase (Intercept) 0.222 0.802 -1.56 0.118
## 5 f0Increase ForeheadtempDecrease 1.00 96.5 0.108 0.914
## 6 f0Increase ForeheadtempIncrease 0.553 0.932 0.227 0.821emmeans(m, pairwise~Forehead, by="change")## $emmeans
## change = f0NS:
## Forehead prob SE df lower.CL upper.CL
## tempNS 7.00e-01 0.14492 6 0.34539 1.05458
## tempDecrease 1.07e-04 0.01036 6 -0.02524 0.02546
## tempIncrease 6.30e-01 0.09294 6 0.40222 0.85703
##
## change = f0Decrease:
## Forehead prob SE df lower.CL upper.CL
## tempNS 1.00e-01 0.09487 6 -0.13213 0.33213
## tempDecrease 4.70e-06 0.00217 6 -0.00531 0.00532
## tempIncrease 1.48e-01 0.06837 6 -0.01914 0.31544
##
## change = f0Increase:
## Forehead prob SE df lower.CL upper.CL
## tempNS 2.00e-01 0.12649 6 -0.10951 0.50953
## tempDecrease 1.00e+00 0.01059 6 0.97399 1.02579
## tempIncrease 2.22e-01 0.08001 6 0.02645 0.41800
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.6999 0.1453 6 4.817 0.0070
## tempNS - tempIncrease 0.0704 0.1722 6 0.409 0.9133
## tempDecrease - tempIncrease -0.6295 0.0935 6 -6.732 0.0013
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.1000 0.0949 6 1.054 0.5736
## tempNS - tempIncrease -0.0481 0.1169 6 -0.412 0.9121
## tempDecrease - tempIncrease -0.1481 0.0684 6 -2.166 0.1564
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.7999 0.1269 6 -6.301 0.0018
## tempNS - tempIncrease -0.0222 0.1497 6 -0.148 0.9880
## tempDecrease - tempIncrease 0.7777 0.0807 6 9.636 0.0002
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of nose temperature change on f0 SD change
ggplot(ts |> filter(section=="entireExp", feature=="f0SD"), aes(Nose, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Nose temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Nose, data=ts |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 32.785224
## iter 20 value 32.732617
## iter 30 value 32.729163
## final value 32.729158
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.000144 83.4 -0.106 0.916
## 2 f0Decrease NosetempDecrease 0.00945 435. -0.0107 0.991
## 3 f0Decrease NosetempIncrease 0.999 83.4 0.0901 0.928
## 4 f0Increase (Intercept) 0.500 1.41 0.000155 1.00
## 5 f0Increase NosetempDecrease 0.200 1.80 -0.769 0.442
## 6 f0Increase NosetempIncrease 0.269 1.48 -0.674 0.500emmeans(m, pairwise~Nose, by="change")## $emmeans
## change = f0NS:
## Nose prob SE df lower.CL upper.CL
## tempNS 5.00e-01 0.353553 6 -0.36520 1.36502
## tempDecrease 8.00e-01 0.178885 6 0.36229 1.23772
## tempIncrease 6.13e-01 0.087483 6 0.39884 0.82697
##
## change = f0Decrease:
## Nose prob SE df lower.CL upper.CL
## tempNS 7.19e-05 0.005995 6 -0.01460 0.01474
## tempDecrease 1.10e-06 0.000468 6 -0.00115 0.00115
## tempIncrease 1.61e-01 0.066058 6 -0.00035 0.32293
##
## change = f0Increase:
## Nose prob SE df lower.CL upper.CL
## tempNS 5.00e-01 0.353553 6 -0.36510 1.36513
## tempDecrease 2.00e-01 0.178884 6 -0.23772 0.63771
## tempIncrease 2.26e-01 0.075095 6 0.04206 0.40956
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -3.00e-01 0.39623 6 -0.757 0.7406
## tempNS - tempIncrease -1.13e-01 0.36422 6 -0.310 0.9488
## tempDecrease - tempIncrease 1.87e-01 0.19913 6 0.940 0.6375
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 7.08e-05 0.00601 6 0.012 0.9999
## tempNS - tempIncrease -1.61e-01 0.06633 6 -2.431 0.1115
## tempDecrease - tempIncrease -1.61e-01 0.06606 6 -2.442 0.1100
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 3.00e-01 0.39623 6 0.757 0.7407
## tempNS - tempIncrease 2.74e-01 0.36144 6 0.759 0.7399
## tempDecrease - tempIncrease -2.58e-02 0.19401 6 -0.133 0.9903
##
## P value adjustment: tukey method for comparing a family of 3 estimatesNo effect of eye temperature change on f0 SD change
ggplot(ts |> filter(section=="entireExp", feature=="f0SD", !is.na(Eyes)), aes(Eyes, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Eye temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Eyes, data=ts |> filter(feature=="f0SD", section=="entireExp", !is.na(Eyes)))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 36.254206
## iter 10 value 26.624975
## iter 20 value 26.610238
## iter 30 value 26.609178
## final value 26.609178
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.00000996 1.42e+2 -0.0813 0.935
## 2 f0Decrease EyestempDecrease 0.5 1.31e-9 0 1
## 3 f0Decrease EyestempIncrease 1.00 1.42e+2 0.0690 0.945
## 4 f0Increase (Intercept) 0.167 1.10e+0 -1.47 0.142
## 5 f0Increase EyestempDecrease 0.5 0 NaN NaN
## 6 f0Increase EyestempIncrease 0.673 1.18e+0 0.610 0.542# emmeans(m, pairwise~Eyes, by="change") # isn't working (I'm thinking maybe because "eyes" have lots of missing values)No effect of cheek temperature change on f0 SD change
ggplot(ts |> filter(section=="entireExp", feature=="f0SD"), aes(Cheeks, fill=change))+
geom_bar()+
scale_fill_manual(values = c("f0Decrease" = "lightblue", "f0Increase" = "red", "f0NS" = "gray"))+
ggtitle("Cheek temp. change and f0 SD change")
tidy(m <- nnet::multinom(change ~ Cheeks, data=ts |> filter(feature=="f0SD", section=="entireExp"))) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 12 (6 variable)
## initial value 41.747267
## iter 10 value 30.881893
## iter 20 value 30.849472
## final value 30.847366
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f0Decrease (Intercept) 0.0909 1.05 -2.20 0.0281
## 2 f0Decrease CheekstempDecrease 0.000189 86.9 -0.0986 0.921
## 3 f0Decrease CheekstempIncrease 0.851 1.22 1.43 0.154
## 4 f0Increase (Intercept) 0.333 0.548 -1.27 0.206
## 5 f0Increase CheekstempDecrease 0.364 0.971 -0.576 0.564
## 6 f0Increase CheekstempIncrease 0.364 0.971 -0.576 0.564emmeans(m, pairwise~Cheeks, by="change")## $emmeans
## change = f0NS:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 6.25e-01 0.12103 6 0.32883 0.92114
## tempDecrease 7.78e-01 0.13859 6 0.43864 1.11686
## tempIncrease 5.38e-01 0.13826 6 0.20014 0.87678
##
## change = f0Decrease:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 6.25e-02 0.06052 6 -0.08558 0.21059
## tempDecrease 1.47e-05 0.00128 6 -0.00311 0.00314
## tempIncrease 3.08e-01 0.12801 6 -0.00552 0.62093
##
## change = f0Increase:
## Cheeks prob SE df lower.CL upper.CL
## tempNS 3.13e-01 0.11588 6 0.02896 0.59606
## tempDecrease 2.22e-01 0.13858 6 -0.11686 0.56134
## tempIncrease 1.54e-01 0.10007 6 -0.09101 0.39869
##
## Confidence level used: 0.95
##
## $contrasts
## change = f0NS:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease -0.1528 0.1840 6 -0.830 0.6996
## tempNS - tempIncrease 0.0865 0.1838 6 0.471 0.8872
## tempDecrease - tempIncrease 0.2393 0.1958 6 1.222 0.4840
##
## change = f0Decrease:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0625 0.0605 6 1.032 0.5854
## tempNS - tempIncrease -0.2452 0.1416 6 -1.732 0.2695
## tempDecrease - tempIncrease -0.3077 0.1280 6 -2.404 0.1154
##
## change = f0Increase:
## contrast estimate SE df t.ratio p.value
## tempNS - tempDecrease 0.0903 0.1806 6 0.500 0.8741
## tempNS - tempIncrease 0.1587 0.1531 6 1.036 0.5832
## tempDecrease - tempIncrease 0.0684 0.1709 6 0.400 0.9167
##
## P value adjustment: tukey method for comparing a family of 3 estimatesAll speakers pulled together
f0 median
- Across entire experiment: f0 median increases in both conditions.
- Only during question lists: f0 median doesn’t change in any condition.
- Only during Diapix: f0 median increases in both conditions.
load(paste0(here::here(), "/data/speechData-allIPUs.RData"))
# ipus <- ipus |>
# filter(gender != "Male")ggplot(ipus, aes(turnOverall, f0medz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Entire experiment")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 188 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 188 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14062.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6272 -0.6559 -0.1142 0.5397 4.9370
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.001996 0.04468
## Residual 0.937662 0.96833
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.193e-02 2.743e-02 3.421e+02 -1.893 0.059210 .
## turnOverall 1.056e-03 3.091e-04 4.341e+03 3.417 0.000639 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.825summary(m1 <- lmer(f0medz ~ turnOverall : condition + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:condition + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14073.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6239 -0.6542 -0.1149 0.5345 4.9450
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.002054 0.04532
## Residual 0.937313 0.96815
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -5.606e-02 2.757e-02 3.361e+02 -2.033
## turnOverall:conditionclose 1.431e-03 3.849e-04 7.182e+02 3.717
## turnOverall:conditionimpersonal 8.505e-04 3.342e-04 8.997e+02 2.545
## Pr(>|t|)
## (Intercept) 0.042818 *
## turnOverall:conditionclose 0.000217 ***
## turnOverall:conditionimpersonal 0.011091 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.713
## trnOvrll:cndtnm -0.724 0.516anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14049 14076 -7020.7 14041
## m1 5 14049 14081 -7019.4 14039 2.6732 1 0.102par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Lists"), aes(turnOverall, f0medz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Question lists (1st part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 42 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 42 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 3938.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4269 -0.6360 -0.1422 0.5219 4.5342
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.03997 0.1999
## Residual 1.00093 1.0005
## Number of obs: 1371, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.710e-02 5.928e-02 1.329e+02 -0.626 0.532
## turnOverall -4.771e-04 2.053e-03 1.193e+03 -0.232 0.816
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.692summary(m1 <- lmer(f0medz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 3947.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4275 -0.6364 -0.1414 0.5211 4.5292
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.04061 0.2015
## Residual 1.00141 1.0007
## Number of obs: 1371, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -3.518e-02 6.032e-02 1.330e+02 -0.583
## turnOverall:conditionclose -9.304e-04 3.248e-03 3.872e+02 -0.286
## turnOverall:conditionimpersonal -3.484e-04 2.189e-03 6.449e+02 -0.159
## Pr(>|t|)
## (Intercept) 0.561
## turnOverall:conditionclose 0.775
## turnOverall:conditionimpersonal 0.874
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.562
## trnOvrll:cndtnm -0.579 0.325anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 3931.2 3952.0 -1961.6 3923.2
## m1 5 3933.1 3959.2 -1961.6 3923.1 0.0401 1 0.8412par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Diapix"), aes(turnOverall, f0medz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Diapix (2nd part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 10074.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4970 -0.6472 -0.0909 0.5529 4.6796
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01985 0.1409
## Residual 0.88424 0.9403
## Number of obs: 3689, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.237e-02 5.208e-02 2.932e+02 -1.198 0.232
## turnOverall 1.192e-03 4.741e-04 2.854e+03 2.514 0.012 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.841summary(m1 <- lmer(f0medz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 10087
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4967 -0.6501 -0.0881 0.5534 4.6895
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01951 0.1397
## Residual 0.88434 0.9404
## Number of obs: 3689, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -6.617e-02 5.213e-02 3.119e+02 -1.269
## turnOverall:conditionclose 1.513e-03 5.741e-04 9.141e+02 2.635
## turnOverall:conditionimpersonal 9.963e-04 5.118e-04 5.802e+02 1.947
## Pr(>|t|)
## (Intercept) 0.20533
## turnOverall:conditionclose 0.00855 **
## turnOverall:conditionimpersonal 0.05207 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.737
## trnOvrll:cndtnm -0.748 0.552anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10064 10089 -5027.9 10056
## m1 5 10065 10096 -5027.4 10055 1.0392 1 0.308par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
Checking if f0 median changes across time in interaction with questionnaire scores: Even though many of the models are significant, their AIC is not preferred over the model without the questionnaire scores.
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14062.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6272 -0.6559 -0.1142 0.5397 4.9370
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.001996 0.04468
## Residual 0.937662 0.96833
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.193e-02 2.743e-02 3.421e+02 -1.893 0.059210 .
## turnOverall 1.056e-03 3.091e-04 4.341e+03 3.417 0.000639 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.825summary(m1 <- lmer(f0medz ~ turnOverall : closeness + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:closeness + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14073.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6631 -0.6588 -0.1074 0.5381 4.9145
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.002762 0.05256
## Residual 0.938625 0.96883
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.905e-03 2.317e-02 7.737e+01 -0.298 0.7665
## turnOverall:closeness 1.189e-04 6.052e-05 2.565e+02 1.964 0.0506 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.715summary(m2 <- lmer(f0medz ~ turnOverall : similarity + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:similarity + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14075.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6546 -0.6584 -0.1077 0.5365 4.9077
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.002078 0.04559
## Residual 0.939356 0.96920
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.552e-03 2.537e-02 1.504e+02 -0.179 0.858
## turnOverall:similarity 7.349e-05 4.906e-05 8.452e+02 1.498 0.135
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.789summary(m3 <- lmer(f0medz ~ turnOverall : likeability + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:likeability + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14071.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6419 -0.6596 -0.1082 0.5360 4.9234
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.002234 0.04727
## Residual 0.938452 0.96874
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.936e-02 2.658e-02 2.130e+02 -1.105 0.2705
## turnOverall:likeability 1.109e-04 4.334e-05 1.904e+03 2.559 0.0106 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.806summary(m4 <- lmer(f0medz ~ turnOverall : becomeFriends + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14073.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6524 -0.6586 -0.1082 0.5371 4.9203
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.002421 0.04921
## Residual 0.938759 0.96890
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.560e-02 2.552e-02 1.477e+02 -0.612 0.5418
## turnOverall:becomeFriends 9.565e-05 4.640e-05 8.303e+02 2.061 0.0396 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.782anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14049 14076 -7020.7 14041
## m1 4 14058 14084 -7024.8 14050 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m2: f0medz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14049 14076 -7020.7 14041
## m2 4 14059 14085 -7025.4 14051 0 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m3: f0medz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14049 14076 -7020.7 14041
## m3 4 14055 14081 -7023.3 14047 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m4: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14049 14076 -7020.7 14041
## m4 4 14057 14083 -7024.5 14049 0 0f0 max
- Across entire experiment: f0 max decreases in both conditions.
- Only during question lists: f0 max tends to decrease only in the
impersonal condition, but if we’re being strict (AIC of model
with
conditionwas 1.7 smaller than without), there f0 max went down in both conditions. - Only during Diapix: f0 max doesn’t change significantly for either group.
load(paste0(here::here(), "/data/speechData-allIPUs.RData"))
# ipus <- ipus |>
# filter(gender != "Male")ggplot(ipus, aes(turnOverall, f0maxz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Entire experiment")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 111 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 111 rows containing missing values (`geom_point()`).
summary(m <- lm(f0maxz ~ turnOverall, ipus))##
## Call:
## lm(formula = f0maxz ~ turnOverall, data = ipus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6147 -0.6920 -0.1241 0.5394 4.7085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.134455 0.026883 5.001 5.88e-07 ***
## turnOverall -0.001669 0.000313 -5.331 1.02e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9929 on 5135 degrees of freedom
## (111 observations deleted due to missingness)
## Multiple R-squared: 0.005504, Adjusted R-squared: 0.00531
## F-statistic: 28.42 on 1 and 5135 DF, p-value: 1.019e-07summary(m1 <- lm(f0maxz ~ turnOverall : condition, ipus))##
## Call:
## lm(formula = f0maxz ~ turnOverall:condition, data = ipus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6120 -0.6928 -0.1237 0.5413 4.7147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1350865 0.0270132 5.001 5.90e-07 ***
## turnOverall:conditionclose -0.0017212 0.0003811 -4.516 6.44e-06 ***
## turnOverall:conditionimpersonal -0.0016420 0.0003323 -4.942 7.98e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.993 on 5134 degrees of freedom
## (111 observations deleted due to missingness)
## Multiple R-squared: 0.005515, Adjusted R-squared: 0.005128
## F-statistic: 14.24 on 2 and 5134 DF, p-value: 6.832e-07anova(m, m1)## Analysis of Variance Table
##
## Model 1: f0maxz ~ turnOverall
## Model 2: f0maxz ~ turnOverall:condition
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 5135 5062.8
## 2 5134 5062.8 1 0.057298 0.0581 0.8095par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Lists"), aes(turnOverall, f0maxz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Question lists (1st part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 23 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 23 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 4166.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4119 -0.6898 -0.1841 0.5046 4.1633
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.03965 0.1991
## Residual 1.13682 1.0662
## Number of obs: 1390, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.425e-01 6.168e-02 1.251e+02 5.554 1.60e-07 ***
## turnOverall -9.977e-03 2.167e-03 1.150e+03 -4.603 4.62e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.704summary(m1 <- lmer(f0maxz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 4171.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3679 -0.6888 -0.1690 0.5041 4.1652
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.05317 0.2306
## Residual 1.12918 1.0626
## Number of obs: 1390, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.321492 0.065312 97.257020 4.922
## turnOverall:conditionclose -0.004438 0.003454 338.987109 -1.285
## turnOverall:conditionimpersonal -0.011863 0.002337 591.864519 -5.075
## Pr(>|t|)
## (Intercept) 3.49e-06 ***
## turnOverall:conditionclose 0.2
## turnOverall:conditionimpersonal 5.19e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.546
## trnOvrll:cndtnm -0.558 0.304anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 4159.4 4180.4 -2075.7 4151.4
## m1 5 4157.7 4183.9 -2073.8 4147.7 3.7813 1 0.05183 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Diapix"), aes(turnOverall, f0maxz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Diapix (2nd part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 88 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 88 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 10288.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8069 -0.6934 -0.1074 0.5672 4.9201
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.009223 0.09604
## Residual 0.901496 0.94947
## Number of obs: 3747, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.206e-03 4.867e-02 4.109e+02 -0.086 0.931
## turnOverall -3.555e-04 4.674e-04 2.054e+03 -0.760 0.447
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.888summary(m1 <- lmer(f0maxz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 10301.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8077 -0.6947 -0.1077 0.5669 4.9243
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.009725 0.09862
## Residual 0.901501 0.94947
## Number of obs: 3747, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -3.374e-03 4.917e-02 4.492e+02 -0.069
## turnOverall:conditionclose -4.092e-04 5.548e-04 8.961e+02 -0.737
## turnOverall:conditionimpersonal -3.279e-04 4.894e-04 5.851e+02 -0.670
## Pr(>|t|)
## (Intercept) 0.945
## turnOverall:conditionclose 0.461
## turnOverall:conditionimpersonal 0.503
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.803
## trnOvrll:cndtnm -0.810 0.650anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10277 10302 -5134.5 10269
## m1 5 10279 10310 -5134.5 10269 0.027 1 0.8694par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
Checking if f0 SD changes across time in interaction with questionnaire scores: Even though many of the models are significant, their AIC is not preferred over the model without the questionnaire scores.
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14524.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6404 -0.6969 -0.1250 0.5432 4.7420
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.0000 0.0000
## Residual 0.9859 0.9929
## Number of obs: 5137, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.345e-01 2.688e-02 5.135e+03 5.001 5.88e-07 ***
## turnOverall -1.669e-03 3.130e-04 5.135e+03 -5.331 1.02e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.857
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0maxz ~ turnOverall : closeness + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:closeness + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14539.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6395 -0.6847 -0.1290 0.5350 4.7623
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.0000 0.0000
## Residual 0.9881 0.9941
## Number of obs: 5137, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.729e-02 2.114e-02 5.135e+03 3.656 0.000259 ***
## turnOverall:closeness -2.371e-04 5.764e-05 5.135e+03 -4.115 3.94e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.755
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m2 <- lmer(f0maxz ~ turnOverall : similarity + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:similarity + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14531.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6757 -0.6894 -0.1292 0.5387 4.8003
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.0000 0.0000
## Residual 0.9865 0.9932
## Number of obs: 5137, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.116e-01 2.422e-02 5.135e+03 4.607 4.19e-06 ***
## turnOverall:similarity -2.440e-04 4.850e-05 5.135e+03 -5.031 5.05e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.820
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m3 <- lmer(f0maxz ~ turnOverall : likeability + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:likeability + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14530.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6772 -0.6917 -0.1282 0.5436 4.7688
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.0000 0.0000
## Residual 0.9863 0.9931
## Number of obs: 5137, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.229e-01 2.560e-02 5.135e+03 4.800 1.63e-06 ***
## turnOverall:likeability -2.239e-04 4.333e-05 5.135e+03 -5.167 2.46e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.841
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m4 <- lmer(f0maxz ~ turnOverall : becomeFriends + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14533.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6748 -0.6896 -0.1252 0.5400 4.7221
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.0000 0.0000
## Residual 0.9869 0.9935
## Number of obs: 5137, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.070e-01 2.417e-02 5.135e+03 4.428 9.72e-06 ***
## turnOverall:becomeFriends -2.202e-04 4.572e-05 5.135e+03 -4.817 1.50e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.819
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce q## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14512 14538 -7251.7 14504
## m1 4 14523 14549 -7257.5 14515 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce q## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m2: f0maxz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14512 14538 -7251.7 14504
## m2 4 14515 14541 -7253.3 14507 0 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce q## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m3: f0maxz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14512 14538 -7251.7 14504
## m3 4 14513 14539 -7252.6 14505 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce q## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m4: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14512 14538 -7251.7 14504
## m4 4 14517 14543 -7254.3 14509 0 0f0 SD
- Across entire experiment: f0 SD doesn’t change in either condition.
- Only during question lists: f0 SD decreases in both conditions.
- Only during Diapix: f0 SD doesn’t change in either condition.
load(paste0(here::here(), "/data/speechData-allIPUs.RData"))
# ipus <- ipus |>
# filter(gender != "Male")ggplot(ipus, aes(turnOverall, f0sdz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Entire experiment")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 268 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 268 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14251.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2064 -0.7197 -0.0992 0.6332 5.2302
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00 0.00
## Residual 1.02 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.632e-03 2.770e-02 4.978e+03 0.312 0.755
## turnOverall 4.886e-04 3.222e-04 4.978e+03 1.516 0.130
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.856
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0sdz ~ turnOverall : condition + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:condition + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14264.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1840 -0.7153 -0.0976 0.6378 5.2186
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00 0.00
## Residual 1.02 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.495e-03 2.783e-02 4.977e+03 0.197 0.8435
## turnOverall:conditionclose 7.540e-04 3.930e-04 4.977e+03 1.919 0.0551
## turnOverall:conditionimpersonal 3.536e-04 3.419e-04 4.977e+03 1.034 0.3011
##
## (Intercept)
## turnOverall:conditionclose .
## turnOverall:conditionimpersonal
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.753
## trnOvrll:cndtnm -0.771 0.581
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14239 14265 -7115.3 14231
## m1 5 14239 14272 -7114.7 14229 1.3923 1 0.238par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Lists"), aes(turnOverall, f0sdz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Question lists (1st part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 56 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 56 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 3805.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4356 -0.6830 -0.1016 0.6261 4.2131
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.08148 0.2854
## Residual 0.92134 0.9599
## Number of obs: 1357, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.324e-01 6.675e-02 8.774e+01 1.984 0.0504 .
## turnOverall -8.838e-03 2.020e-03 1.314e+03 -4.376 1.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.593summary(m1 <- lmer(f0sdz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 3813.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4423 -0.6955 -0.1152 0.6227 4.1930
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07613 0.2759
## Residual 0.92253 0.9605
## Number of obs: 1357, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.143081 0.066480 89.423394 2.152
## turnOverall:conditionclose -0.011669 0.003309 521.764928 -3.526
## turnOverall:conditionimpersonal -0.007833 0.002211 784.553139 -3.543
## Pr(>|t|)
## (Intercept) 0.034073 *
## turnOverall:conditionclose 0.000459 ***
## turnOverall:conditionimpersonal 0.000419 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.485
## trnOvrll:cndtnm -0.481 0.233anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 3798.7 3819.5 -1895.3 3790.7
## m1 5 3799.5 3825.6 -1894.7 3789.5 1.2041 1 0.2725par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Diapix"), aes(turnOverall, f0sdz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Diapix (2nd part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 212 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 212 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 10354.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2442 -0.7081 -0.0770 0.6328 5.2415
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01369 0.117
## Residual 1.00686 1.003
## Number of obs: 3623, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.302e-01 5.327e-02 3.858e+02 2.444 0.015 *
## turnOverall -6.091e-04 5.039e-04 2.288e+03 -1.209 0.227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.876summary(m1 <- lmer(f0sdz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 10367.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2384 -0.7064 -0.0772 0.6340 5.2410
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01384 0.1176
## Residual 1.00704 1.0035
## Number of obs: 3623, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.283e-01 5.360e-02 4.113e+02 2.394
## turnOverall:conditionclose -4.946e-04 6.009e-04 8.911e+02 -0.823
## turnOverall:conditionimpersonal -6.688e-04 5.314e-04 5.851e+02 -1.259
## Pr(>|t|)
## (Intercept) 0.0171 *
## turnOverall:conditionclose 0.4107
## turnOverall:conditionimpersonal 0.2087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.785
## trnOvrll:cndtnm -0.794 0.624anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10343 10368 -5167.7 10335
## m1 5 10345 10376 -5167.6 10335 0.1511 1 0.6975par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
Checking if f0 SD changes across time in interaction with questionnaire scores: Even though many of the models are significant, their AIC is not preferred over the model without the questionnaire scores.
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14251.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2064 -0.7197 -0.0992 0.6332 5.2302
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00 0.00
## Residual 1.02 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.632e-03 2.770e-02 4.978e+03 0.312 0.755
## turnOverall 4.886e-04 3.222e-04 4.978e+03 1.516 0.130
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.856
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0sdz ~ turnOverall : closeness + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:closeness + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14256.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1720 -0.7223 -0.0976 0.6327 5.2175
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.000 0.00
## Residual 1.021 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.717e-02 2.178e-02 4.978e+03 1.247 0.212
## turnOverall:closeness 6.289e-05 5.926e-05 4.978e+03 1.061 0.289
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.754
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m2 <- lmer(f0sdz ~ turnOverall : similarity + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:similarity + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14257.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1771 -0.7207 -0.0971 0.6346 5.2196
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.000 0.00
## Residual 1.021 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.982e-02 2.498e-02 4.978e+03 1.194 0.233
## turnOverall:similarity 3.607e-05 4.995e-05 4.978e+03 0.722 0.470
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.819
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m3 <- lmer(f0sdz ~ turnOverall : likeability + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:likeability + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14256.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1837 -0.7210 -0.0967 0.6353 5.2240
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00 0.00
## Residual 1.02 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.767e-02 2.638e-02 4.978e+03 0.670 0.503
## turnOverall:likeability 5.421e-05 4.462e-05 4.978e+03 1.215 0.224
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.840
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m4 <- lmer(f0sdz ~ turnOverall : becomeFriends + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 14255.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1919 -0.7221 -0.0989 0.6328 5.2284
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00 0.00
## Residual 1.02 1.01
## Number of obs: 4980, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.437e-02 2.491e-02 4.978e+03 0.577 0.564
## turnOverall:becomeFriends 6.978e-05 4.707e-05 4.978e+03 1.482 0.138
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.818
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14239 14265 -7115.3 14231
## m1 4 14240 14266 -7115.9 14232 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m2: f0sdz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14239 14265 -7115.3 14231
## m2 4 14240 14266 -7116.2 14232 0 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m3: f0sdz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14239 14265 -7115.3 14231
## m3 4 14240 14266 -7115.8 14232 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m4: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 14239 14265 -7115.3 14231
## m4 4 14239 14265 -7115.4 14231 0 0All f0 measures & IPU length
(These analyses are using the non-z-scored values.)
f0 median: the regression suggests a negative relationship between f0 median and IPU duration, but the residuals are very heteroskedastic and a look at the raw data shows it’s more complex than that.
ggplot(ipus, aes(f0med, ipuDur))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 188 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 188 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0med ~ ipuDur + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0med ~ ipuDur + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 43151.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3777 -0.5956 -0.0971 0.4834 6.1788
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 531.5 23.05
## Residual 284.3 16.86
## Number of obs: 5060, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 188.8551 3.7565 37.3863 50.274 <2e-16 ***
## ipuDur -0.2791 0.1587 5022.9261 -1.759 0.0787 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ipuDur -0.060par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
f0 max: it’s a more robust finding that longer IPUs have higher f0 max values.
ggplot(ipus, aes(f0max, ipuDur))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 111 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 111 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0max ~ ipuDur + (1|speaker), ipus))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0max ~ ipuDur + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53030.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5298 -0.6293 -0.1303 0.4745 4.7527
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1163 34.10
## Residual 1727 41.55
## Number of obs: 5137, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 234.2913 5.5984 38.0074 41.85 <2e-16 ***
## ipuDur 8.8831 0.3894 5103.0170 22.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## ipuDur -0.098par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
f0 SD: again, a positive relation is suggested between f0 SD and IPU duration, but the plot shows it isn’t linear, and the residuals are again very heteroskedastic.
ggplot(ipus, aes(f0sd, ipuDur))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 268 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 268 rows containing missing values (`geom_point()`).
summary(m <- lm(f0sd ~ ipuDur, ipus))##
## Call:
## lm(formula = f0sd ~ ipuDur, data = ipus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.127 -7.932 -1.829 5.840 48.283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.22007 0.20696 92.87 <2e-16 ***
## ipuDur 1.11075 0.09988 11.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.79 on 4978 degrees of freedom
## (268 observations deleted due to missingness)
## Multiple R-squared: 0.02424, Adjusted R-squared: 0.02404
## F-statistic: 123.7 on 1 and 4978 DF, p-value: < 2.2e-16par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
Without domino task
f0 median
- Across entire experiment: f0 median increases in both conditions.
- Only during question lists: f0 median doesn’t change in any condition.
- Only during Diapix: f0 median increases in both conditions (the model suggests there’s a difference between conditions but it doesn’t seem true).
ggplot(ipus, aes(turnOverall, f0medz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Entire experiment")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 704 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 704 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus)) # the model is singular, so adding or removing random effects isn't making any difference## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3004 -0.6565 -0.1051 0.5512 4.5987
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.752e-31 6.125e-16
## Residual 9.904e-01 9.952e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.820e-02 1.461e-02 1.871e+04 -4.667 3.08e-06 ***
## turnOverall 8.849e-04 1.644e-04 1.871e+04 5.382 7.47e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.867
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0medz ~ turnOverall : condition + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:condition + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52936.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3201 -0.6591 -0.1020 0.5531 4.6134
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.750e-31 6.124e-16
## Residual 9.899e-01 9.949e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -7.521e-02 1.475e-02 1.870e+04 -5.099
## turnOverall:conditionclose 1.287e-03 2.015e-04 1.870e+04 6.386
## turnOverall:conditionimpersonal 7.099e-04 1.720e-04 1.870e+04 4.127
## Pr(>|t|)
## (Intercept) 3.45e-07 ***
## turnOverall:conditionclose 1.74e-10 ***
## turnOverall:conditionimpersonal 3.70e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.780
## trnOvrll:cndtnm -0.780 0.609
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52917 52949 -26455 52909
## m1 5 52908 52947 -26449 52898 11.893 1 0.0005636 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Lists"), aes(turnOverall, f0medz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Question lists (1st part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 120 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 120 rows containing missing values (`geom_point()`).
# ggsave(paste0(here::here(), "/figures/forMeeting/medianCondition.png"))
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11311
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8741 -0.6573 -0.1154 0.5114 4.1135
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07897 0.281
## Residual 0.99991 1.000
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.770e-02 5.254e-02 5.164e+01 -1.289 0.203
## turnOverall 1.173e-03 1.289e-03 3.823e+03 0.911 0.363
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.379summary(m1 <- lmer(f0medz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11320.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8705 -0.6584 -0.1149 0.5190 4.0943
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07579 0.2753
## Residual 1.00024 1.0001
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -6.356e-02 5.190e-02 5.136e+01 -1.225
## turnOverall:conditionclose -2.206e-04 1.884e-03 1.902e+03 -0.117
## turnOverall:conditionimpersonal 2.241e-03 1.647e-03 2.413e+03 1.361
## Pr(>|t|)
## (Intercept) 0.226
## turnOverall:conditionclose 0.907
## turnOverall:conditionimpersonal 0.174
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.321
## trnOvrll:cndtnm -0.250 0.080anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 11303 11328 -5647.7 11295
## m1 5 11304 11336 -5647.1 11294 1.0686 1 0.3013par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Diapix"), aes(turnOverall, f0medz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Diapix (2nd part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 584 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 584 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41322.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4302 -0.6501 -0.0791 0.5608 4.4185
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.006863 0.08284
## Residual 0.958730 0.97915
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.227e-01 2.795e-02 2.602e+02 -4.392 1.64e-05 ***
## turnOverall 1.388e-03 2.481e-04 7.223e+03 5.595 2.29e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.819summary(m1 <- lmer(f0medz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Diapix"))) # this was the preferred model, even if the effect for both ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41331.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4261 -0.6511 -0.0794 0.5620 4.4311
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005483 0.07405
## Residual 0.958811 0.97919
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.260e-01 2.725e-02 3.133e+02 -4.623
## turnOverall:conditionclose 1.781e-03 2.996e-04 1.048e+03 5.947
## turnOverall:conditionimpersonal 1.122e-03 2.672e-04 6.240e+02 4.200
## Pr(>|t|)
## (Intercept) 5.52e-06 ***
## turnOverall:conditionclose 3.72e-09 ***
## turnOverall:conditionimpersonal 3.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.733
## trnOvrll:cndtnm -0.744 0.546anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 41309 41339 -20651 41301
## m1 5 41305 41343 -20648 41295 5.6208 1 0.01775 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(2, 2))
hist(resid(m1))
qqnorm(resid(m1));qqline(resid(m1))
plot(fitted(m1), resid(m1))
Checking if f0 median changes across time in interaction with questionnaire scores: Even though many of the models are significant, their AIC is not preferred over the model without the questionnaire scores.
# entire experiment
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3004 -0.6565 -0.1051 0.5512 4.5987
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.752e-31 6.125e-16
## Residual 9.904e-01 9.952e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.820e-02 1.461e-02 1.871e+04 -4.667 3.08e-06 ***
## turnOverall 8.849e-04 1.644e-04 1.871e+04 5.382 7.47e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.867
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0medz ~ turnOverall : closeness + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:closeness + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52942.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2973 -0.6552 -0.0977 0.5479 4.5778
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.753e-31 6.126e-16
## Residual 9.907e-01 9.954e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.217e-02 1.138e-02 1.871e+04 -3.706 0.000211 ***
## turnOverall:closeness 1.441e-04 2.989e-05 1.871e+04 4.821 1.44e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.769
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m2 <- lmer(f0medz ~ turnOverall : similarity + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:similarity + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52954.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2906 -0.6519 -0.1066 0.5458 4.5780
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.756e-31 6.128e-16
## Residual 9.914e-01 9.957e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.603e-02 1.312e-02 1.871e+04 -2.746 0.006044 **
## turnOverall:similarity 8.371e-05 2.536e-05 1.871e+04 3.300 0.000968 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.832
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m3 <- lmer(f0medz ~ turnOverall : likeability + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:likeability + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52944.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2836 -0.6563 -0.1004 0.5456 4.5898
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.754e-31 6.127e-16
## Residual 9.908e-01 9.954e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.491e-02 1.393e-02 1.871e+04 -3.942 8.1e-05 ***
## turnOverall:likeability 1.052e-04 2.276e-05 1.871e+04 4.624 3.8e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.853
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m4 <- lmer(f0medz ~ turnOverall : becomeFriends + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52950.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2867 -0.6518 -0.1022 0.5438 4.5826
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.755e-31 6.128e-16
## Residual 9.912e-01 9.956e-01
## Number of obs: 18708, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.200e-02 1.305e-02 1.871e+04 -3.218 0.001294 **
## turnOverall:becomeFriends 9.237e-05 2.383e-05 1.871e+04 3.877 0.000106 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.830
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52917 52949 -26455 52909
## m1 4 52923 52954 -26458 52915 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m2: f0medz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52917 52949 -26455 52909
## m2 4 52935 52967 -26464 52927 0 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m3: f0medz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52917 52949 -26455 52909
## m3 4 52925 52956 -26459 52917 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m4: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52917 52949 -26455 52909
## m4 4 52931 52963 -26462 52923 0 0# lists
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11311
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8741 -0.6573 -0.1154 0.5114 4.1135
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07897 0.281
## Residual 0.99991 1.000
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.770e-02 5.254e-02 5.164e+01 -1.289 0.203
## turnOverall 1.173e-03 1.289e-03 3.823e+03 0.911 0.363
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.379summary(m1 <- lmer(f0medz ~ turnOverall : closeness + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:closeness + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11313.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8753 -0.6555 -0.1137 0.5105 4.1143
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07968 0.2823
## Residual 0.99964 0.9998
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.239e-02 5.204e-02 4.881e+01 -1.391 0.171
## turnOverall:closeness 3.930e-04 3.112e-04 3.022e+03 1.263 0.207
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.347summary(m2 <- lmer(f0medz ~ turnOverall : similarity + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:similarity + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11314.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8733 -0.6560 -0.1194 0.5120 4.1158
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07749 0.2784
## Residual 1.00002 1.0000
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.903e-02 5.187e-02 4.981e+01 -1.331 0.189
## turnOverall:similarity 2.232e-04 2.189e-04 3.502e+03 1.020 0.308
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.369summary(m3 <- lmer(f0medz ~ turnOverall : likeability + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:likeability + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11314.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8737 -0.6547 -0.1164 0.5127 4.1201
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07806 0.2794
## Residual 0.99983 0.9999
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.399e-02 5.230e-02 5.156e+01 -1.415 0.163
## turnOverall:likeability 2.312e-04 1.881e-04 3.700e+03 1.229 0.219
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.380summary(m4 <- lmer(f0medz ~ turnOverall : becomeFriends + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 11314.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8736 -0.6547 -0.1180 0.5149 4.1146
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.07796 0.2792
## Residual 0.99987 0.9999
## Number of obs: 3952, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.210e-02 5.195e-02 5.013e+01 -1.388 0.171
## turnOverall:becomeFriends 2.437e-04 2.052e-04 3.550e+03 1.187 0.235
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.366anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 11303 11328 -5647.7 11295
## m1 4 11302 11328 -5647.3 11294 0.7622 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m2: f0medz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 11303 11328 -5647.7 11295
## m2 4 11303 11328 -5647.5 11295 0.2123 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m3: f0medz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 11303 11328 -5647.7 11295
## m3 4 11303 11328 -5647.3 11295 0.6837 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m4: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 11303 11328 -5647.7 11295
## m4 4 11303 11328 -5647.4 11295 0.5828 0# diapix
summary(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41322.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4302 -0.6501 -0.0791 0.5608 4.4185
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.006863 0.08284
## Residual 0.958730 0.97915
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.227e-01 2.795e-02 2.602e+02 -4.392 1.64e-05 ***
## turnOverall 1.388e-03 2.481e-04 7.223e+03 5.595 2.29e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.819summary(m1 <- lmer(f0medz ~ turnOverall : closeness + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:closeness + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41330.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4427 -0.6518 -0.0762 0.5612 4.4271
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.007702 0.08776
## Residual 0.958875 0.97922
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.919e-02 2.346e-02 7.470e+01 -3.376 0.00117 **
## turnOverall:closeness 2.475e-04 4.816e-05 3.459e+02 5.139 4.63e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.701summary(m2 <- lmer(f0medz ~ turnOverall : similarity + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:similarity + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41336.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4450 -0.6505 -0.0791 0.5614 4.4198
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.009388 0.09689
## Residual 0.958905 0.97924
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.018e-02 2.730e-02 1.157e+02 -3.304 0.00127 **
## turnOverall:similarity 1.857e-04 3.982e-05 1.417e+03 4.662 3.42e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.749summary(m3 <- lmer(f0medz ~ turnOverall : likeability + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:likeability + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41332.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4366 -0.6522 -0.0789 0.5586 4.4240
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.007611 0.08724
## Residual 0.958962 0.97927
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.034e-01 2.727e-02 1.790e+02 -3.790 0.000206 ***
## turnOverall:likeability 1.742e-04 3.462e-05 2.983e+03 5.033 5.12e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.792summary(m4 <- lmer(f0medz ~ turnOverall : becomeFriends + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 41337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4418 -0.6523 -0.0790 0.5623 4.4189
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.008199 0.09055
## Residual 0.959144 0.97936
## Number of obs: 14756, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.766e-02 2.659e-02 1.232e+02 -3.297 0.00128 **
## turnOverall:becomeFriends 1.716e-04 3.755e-05 1.088e+03 4.570 5.42e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.765anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m1: f0medz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 41309 41339 -20651 41301
## m1 4 41314 41345 -20653 41306 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m2: f0medz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 41309 41339 -20651 41301
## m2 4 41320 41351 -20656 41312 0 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m3: f0medz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 41309 41339 -20651 41301
## m3 4 41315 41346 -20654 41307 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0medz ~ turnOverall + (1 | speaker)
## m4: f0medz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 41309 41339 -20651 41301
## m4 4 41320 41351 -20656 41312 0 0f0 max
- Across entire experiment: f0 max decreases in both conditions.
- Only during question lists: f0 max doesn’t change in either condition.
- Only during Diapix: f0 max doesn’t change in either condition.
ggplot(ipus, aes(turnOverall, f0maxz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Entire experiment")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 404 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 404 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53607.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9352 -0.6932 -0.1337 0.5525 5.0215
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.047e-31 3.236e-16
## Residual 9.814e-01 9.907e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.802e-01 1.444e-02 1.901e+04 12.48 <2e-16 ***
## turnOverall -2.340e-03 1.626e-04 1.901e+04 -14.39 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.867
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0maxz ~ turnOverall : condition + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:condition + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53622.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9315 -0.6923 -0.1330 0.5525 5.0227
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.047e-31 3.236e-16
## Residual 9.814e-01 9.907e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.791e-01 1.458e-02 1.900e+04 12.29
## turnOverall:conditionclose -2.277e-03 1.992e-04 1.900e+04 -11.43
## turnOverall:conditionimpersonal -2.367e-03 1.702e-04 1.900e+04 -13.91
## Pr(>|t|)
## (Intercept) <2e-16 ***
## turnOverall:conditionclose <2e-16 ***
## turnOverall:conditionimpersonal <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.781
## trnOvrll:cndtnm -0.780 0.609
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 53592 53623 -26792 53584
## m1 5 53593 53633 -26792 53583 0.2992 1 0.5844par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Lists"), aes(turnOverall, f0maxz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Question lists (1st part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 52 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 52 rows containing missing values (`geom_point()`).
# ggsave(paste0(here::here(), "/figures/forMeeting/maxCondition.png"))
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12013.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5050 -0.6673 -0.1654 0.4841 4.1539
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.114 0.3376
## Residual 1.133 1.0643
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.960e-01 6.135e-02 4.685e+01 4.825 1.53e-05 ***
## turnOverall -3.754e-04 1.367e-03 3.947e+03 -0.275 0.784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.343summary(m1 <- lmer(f0maxz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12023.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5134 -0.6714 -0.1610 0.4829 4.1522
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1179 0.3434
## Residual 1.1325 1.0642
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.941e-01 6.234e-02 4.361e+01 4.718
## turnOverall:conditionclose 2.653e-04 2.012e-03 2.370e+03 0.132
## turnOverall:conditionimpersonal -8.620e-04 1.768e-03 2.869e+03 -0.488
## Pr(>|t|)
## (Intercept) 2.47e-05 ***
## turnOverall:conditionclose 0.895
## turnOverall:conditionimpersonal 0.626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.281
## trnOvrll:cndtnm -0.218 0.061anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 12006 12031 -5999 11998
## m1 5 12008 12039 -5999 11998 0.1454 1 0.7029par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Diapix"), aes(turnOverall, f0maxz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Diapix (2nd part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 352 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 352 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40945.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1123 -0.6992 -0.1036 0.5733 4.5351
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00703 0.08384
## Residual 0.89518 0.94614
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.459e-02 2.723e-02 2.438e+02 -2.005 0.0461 *
## turnOverall -2.519e-04 2.388e-04 7.881e+03 -1.055 0.2914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.809summary(m1 <- lmer(f0maxz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40960
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1095 -0.6986 -0.1022 0.5755 4.5276
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.006649 0.08154
## Residual 0.895305 0.94621
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -5.571e-02 2.707e-02 2.323e+02 -2.058
## turnOverall:conditionclose -1.380e-04 2.926e-04 9.567e+02 -0.472
## turnOverall:conditionimpersonal -3.245e-04 2.626e-04 5.720e+02 -1.236
## Pr(>|t|)
## (Intercept) 0.0407 *
## turnOverall:conditionclose 0.6373
## turnOverall:conditionimpersonal 0.2170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.699
## trnOvrll:cndtnm -0.713 0.498anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40933 40963 -20462 40925
## m1 5 40934 40972 -20462 40924 0.492 1 0.483par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
Checking if f0 SD changes across time in interaction with questionnaire scores: Even though many of the models are significant, their AIC is not preferred over the model without the questionnaire scores. One exception: f0 max seems to have decreased more for people that felt closer to their partner.
# entire experiment
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53607.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9352 -0.6932 -0.1337 0.5525 5.0215
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.047e-31 3.236e-16
## Residual 9.814e-01 9.907e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.802e-01 1.444e-02 1.901e+04 12.48 <2e-16 ***
## turnOverall -2.340e-03 1.626e-04 1.901e+04 -14.39 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.867
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0maxz ~ turnOverall : closeness + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:closeness + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53751.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9999 -0.6909 -0.1359 0.5673 5.1069
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.055e-31 3.248e-16
## Residual 9.887e-01 9.943e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.001e-02 1.127e-02 1.901e+04 6.21 5.40e-10 ***
## turnOverall:closeness -2.396e-04 2.965e-05 1.901e+04 -8.08 6.85e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.769
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m2 <- lmer(f0maxz ~ turnOverall : similarity + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -6.2e+03## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:similarity + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53662.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0109 -0.6883 -0.1325 0.5526 5.0581
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.050e-31 3.241e-16
## Residual 9.841e-01 9.920e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.343e-01 1.297e-02 1.901e+04 10.36 <2e-16 ***
## turnOverall:similarity -3.123e-04 2.508e-05 1.901e+04 -12.45 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.832
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m3 <- lmer(f0maxz ~ turnOverall : likeability + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:likeability + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53642
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9843 -0.6939 -0.1352 0.5593 5.0404
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.049e-31 3.239e-16
## Residual 9.830e-01 9.915e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.560e-01 1.378e-02 1.901e+04 11.32 <2e-16 ***
## turnOverall:likeability -2.989e-04 2.252e-05 1.901e+04 -13.27 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.853
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m4 <- lmer(f0maxz ~ turnOverall : becomeFriends + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -5.8e+03## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 53674.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9605 -0.6875 -0.1378 0.5549 5.0614
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 1.051e-31 3.242e-16
## Residual 9.847e-01 9.923e-01
## Number of obs: 19008, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.285e-01 1.291e-02 1.901e+04 9.954 <2e-16 ***
## turnOverall:becomeFriends -2.830e-04 2.360e-05 1.901e+04 -11.988 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.830
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 53592 53623 -26792 53584
## m1 4 53733 53764 -26862 53725 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m2: f0maxz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 53592 53623 -26792 53584
## m2 4 53643 53675 -26818 53635 0 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m3: f0maxz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 53592 53623 -26792 53584
## m3 4 53622 53654 -26807 53614 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m4: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 53592 53623 -26792 53584
## m4 4 53655 53686 -26823 53647 0 0# lists
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12013.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5050 -0.6673 -0.1654 0.4841 4.1539
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.114 0.3376
## Residual 1.133 1.0643
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.960e-01 6.135e-02 4.685e+01 4.825 1.53e-05 ***
## turnOverall -3.754e-04 1.367e-03 3.947e+03 -0.275 0.784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.343summary(m1 <- lmer(f0maxz ~ turnOverall : closeness + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:closeness + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12015.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4906 -0.6608 -0.1616 0.4832 4.1604
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1153 0.3396
## Residual 1.1324 1.0641
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.760e-01 6.101e-02 4.448e+01 4.524 4.48e-05 ***
## turnOverall:closeness 2.453e-04 3.315e-04 3.348e+03 0.740 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.314summary(m2 <- lmer(f0maxz ~ turnOverall : similarity + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:similarity + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12016.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5083 -0.6670 -0.1668 0.4835 4.1540
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.114 0.3376
## Residual 1.133 1.0642
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.974e-01 6.110e-02 4.592e+01 4.868 1.37e-05 ***
## turnOverall:similarity -8.276e-05 2.330e-04 3.744e+03 -0.355 0.722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.332summary(m3 <- lmer(f0maxz ~ turnOverall : likeability + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:likeability + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12017.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4998 -0.6668 -0.1632 0.4820 4.1558
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.114 0.3376
## Residual 1.133 1.0643
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.912e-01 6.135e-02 4.676e+01 4.747 1.98e-05 ***
## turnOverall:likeability -9.721e-06 1.997e-04 3.870e+03 -0.049 0.961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.343summary(m4 <- lmer(f0maxz ~ turnOverall : becomeFriends + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 12016.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5158 -0.6736 -0.1674 0.4821 4.1516
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1137 0.3371
## Residual 1.1326 1.0642
## Number of obs: 4020, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.037e-01 6.097e-02 4.578e+01 4.982 9.44e-06 ***
## turnOverall:becomeFriends -1.466e-04 2.181e-04 3.766e+03 -0.672 0.502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.330anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 12006 12031 -5999.0 11998
## m1 4 12006 12031 -5998.8 11998 0.4604 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m2: f0maxz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 12006 12031 -5999 11998
## m2 4 12006 12031 -5999 11998 0.0509 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m3: f0maxz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 12006 12031 -5999.0 11998
## m3 4 12006 12031 -5999.1 11998 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m4: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 12006 12031 -5999.0 11998
## m4 4 12006 12031 -5998.8 11998 0.3789 0# diapix
summary(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40945.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1123 -0.6992 -0.1036 0.5733 4.5351
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00703 0.08384
## Residual 0.89518 0.94614
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.459e-02 2.723e-02 2.438e+02 -2.005 0.0461 *
## turnOverall -2.519e-04 2.388e-04 7.881e+03 -1.055 0.2914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.809summary(m1 <- lmer(f0maxz ~ turnOverall : closeness + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:closeness + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40950.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1100 -0.6996 -0.1028 0.5752 4.5187
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.006979 0.08354
## Residual 0.895256 0.94618
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.204e-02 2.242e-02 7.994e+01 -3.660 0.000451 ***
## turnOverall:closeness 1.236e-05 4.614e-05 3.616e+02 0.268 0.788943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.703summary(m2 <- lmer(f0maxz ~ turnOverall : similarity + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:similarity + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40948.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1155 -0.6978 -0.1008 0.5729 4.5453
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.007156 0.08459
## Residual 0.895112 0.94610
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.170e-02 2.523e-02 1.361e+02 -2.049 0.0424 *
## turnOverall:similarity -5.090e-05 3.780e-05 1.217e+03 -1.347 0.1783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.770summary(m3 <- lmer(f0maxz ~ turnOverall : likeability + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:likeability + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40949.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1143 -0.6980 -0.1026 0.5719 4.5401
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.007192 0.08481
## Residual 0.895119 0.94611
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.190e-02 2.629e-02 1.814e+02 -1.974 0.0499 *
## turnOverall:likeability -4.162e-05 3.328e-05 3.132e+03 -1.251 0.2112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.790summary(m4 <- lmer(f0maxz ~ turnOverall : becomeFriends + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40949.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1131 -0.6987 -0.1022 0.5724 4.5333
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.007208 0.0849
## Residual 0.895149 0.9461
## Number of obs: 14988, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.834e-02 2.529e-02 1.322e+02 -2.307 0.0226 *
## turnOverall:becomeFriends -3.605e-05 3.596e-05 1.081e+03 -1.003 0.3163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.770anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m1: f0maxz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40933 40963 -20462 40925
## m1 4 40934 40964 -20463 40926 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m2: f0maxz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40933 40963 -20462 40925
## m2 4 40932 40962 -20462 40924 0.6856 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m3: f0maxz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40933 40963 -20462 40925
## m3 4 40932 40963 -20462 40924 0.4272 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0maxz ~ turnOverall + (1 | speaker)
## m4: f0maxz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40933 40963 -20462 40925
## m4 4 40933 40963 -20462 40925 0 0f0 SD
- Across entire experiment: f0 SD doesn’t change in either condition.
- Only during question lists: f0 SD decreases only in close condition.
- Only during Diapix: f0 SD doesn’t change in either condition.
ggplot(ipus, aes(turnOverall, f0sdz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Entire experiment")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1004 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 1004 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52109.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3061 -0.7011 -0.0974 0.6416 5.1757
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.064e-33 9.521e-17
## Residual 9.918e-01 9.959e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.264e-02 1.472e-02 1.841e+04 0.859 0.390
## turnOverall -1.639e-04 1.655e-04 1.841e+04 -0.991 0.322
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.867
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0sdz ~ turnOverall : condition + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:condition + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52114.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2757 -0.7035 -0.0942 0.6419 5.1616
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.059e-33 9.518e-17
## Residual 9.913e-01 9.956e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.992e-03 1.485e-02 1.840e+04 0.403
## turnOverall:conditionclose 2.232e-04 2.030e-04 1.840e+04 1.100
## turnOverall:conditionimpersonal -3.322e-04 1.731e-04 1.840e+04 -1.919
## Pr(>|t|)
## (Intercept) 0.687
## turnOverall:conditionclose 0.271
## turnOverall:conditionimpersonal 0.055 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.779
## trnOvrll:cndtnm -0.780 0.608
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52094 52125 -26043 52086
## m1 5 52085 52124 -26038 52075 10.834 1 0.0009965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
ggplot(ipus |> filter(task=="Lists"), aes(turnOverall, f0sdz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Question lists (1st part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 156 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 156 rows containing missing values (`geom_point()`).
# ggsave(paste0(here::here(), "/figures/forMeeting/sdCondition.png"))
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10427.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6763 -0.6572 -0.0888 0.6277 3.6239
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1777 0.4215
## Residual 0.8119 0.9010
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.657e-02 7.238e-02 4.209e+01 1.334 0.189
## turnOverall -6.234e-03 1.178e-03 3.913e+03 -5.291 1.28e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.249summary(m1 <- lmer(f0sdz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10433.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6347 -0.6463 -0.0878 0.6161 3.6816
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1645 0.4055
## Residual 0.8116 0.9009
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.047e-01 7.003e-02 4.187e+01 1.496
## turnOverall:conditionclose -9.139e-03 1.759e-03 3.322e+03 -5.197
## turnOverall:conditionimpersonal -4.023e-03 1.538e-03 3.567e+03 -2.615
## Pr(>|t|)
## (Intercept) 0.14227
## turnOverall:conditionclose 2.15e-07 ***
## turnOverall:conditionimpersonal 0.00896 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.212
## trnOvrll:cndtnm -0.163 0.035anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10421 10446 -5206.4 10413
## m1 5 10418 10449 -5203.9 10408 4.9395 1 0.02625 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(2, 2))
hist(resid(m1))
qqnorm(resid(m1));qqline(resid(m1))
plot(fitted(m1), resid(m1))
ggplot(ipus |> filter(task=="Diapix"), aes(turnOverall, f0sdz))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~condition)+
ggtitle("Diapix (2nd part of experiment)")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 848 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 848 rows containing missing values (`geom_point()`).
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40934.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3765 -0.7068 -0.0697 0.6413 5.1812
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01269 0.1127
## Residual 0.98114 0.9905
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.401e-02 3.125e-02 1.713e+02 2.048 0.04208 *
## turnOverall -6.827e-04 2.553e-04 1.036e+04 -2.674 0.00752 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.754summary(m1 <- lmer(f0sdz ~ turnOverall : condition + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:condition + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40948.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3765 -0.7064 -0.0693 0.6411 5.1811
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01293 0.1137
## Residual 0.98117 0.9905
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.403e-02 3.138e-02 1.384e+02 2.040
## turnOverall:conditionclose -6.808e-04 3.226e-04 9.392e+02 -2.111
## turnOverall:conditionimpersonal -6.849e-04 2.924e-04 5.732e+02 -2.342
## Pr(>|t|)
## (Intercept) 0.0432 *
## turnOverall:conditionclose 0.0351 *
## turnOverall:conditionimpersonal 0.0195 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trnOvrll:cndtnc
## trnOvrll:cndtnc -0.618
## trnOvrll:cndtnm -0.637 0.394anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:condition + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40922 40952 -20457 40914
## m1 5 40924 40962 -20457 40914 0.0107 1 0.9176par(mfrow=c(2, 2))
hist(resid(m))
qqnorm(resid(m));qqline(resid(m))
plot(fitted(m), resid(m))
Checking if f0 SD changes across time in interaction with questionnaire scores: Even though many of the models are significant, their AIC is not preferred over the model without the questionnaire scores.
# entire experiment
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52109.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3061 -0.7011 -0.0974 0.6416 5.1757
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.064e-33 9.521e-17
## Residual 9.918e-01 9.959e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.264e-02 1.472e-02 1.841e+04 0.859 0.390
## turnOverall -1.639e-04 1.655e-04 1.841e+04 -0.991 0.322
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.867
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m1 <- lmer(f0sdz ~ turnOverall : closeness + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:closeness + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52113.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3174 -0.6999 -0.0974 0.6432 5.1780
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.065e-33 9.521e-17
## Residual 9.918e-01 9.959e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.575e-03 1.145e-02 1.841e+04 -0.487 0.626
## turnOverall:closeness 1.905e-05 3.005e-05 1.841e+04 0.634 0.526
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.768
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m2 <- lmer(f0sdz ~ turnOverall : similarity + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:similarity + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52108.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3083 -0.7027 -0.0975 0.6392 5.1813
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.062e-33 9.519e-17
## Residual 9.915e-01 9.958e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.679e-02 1.321e-02 1.841e+04 2.028 0.0425 *
## turnOverall:similarity -6.220e-05 2.550e-05 1.841e+04 -2.440 0.0147 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.831
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m3 <- lmer(f0sdz ~ turnOverall : likeability + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:likeability + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52113.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3124 -0.6993 -0.0968 0.6399 5.1774
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.064e-33 9.521e-17
## Residual 9.918e-01 9.959e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.281e-02 1.403e-02 1.841e+04 0.913 0.361
## turnOverall:likeability -2.454e-05 2.291e-05 1.841e+04 -1.071 0.284
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.852
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(m4 <- lmer(f0sdz ~ turnOverall : becomeFriends + (1|speaker), ipus))## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: ipus
##
## REML criterion at convergence: 52113.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3126 -0.7006 -0.0975 0.6403 5.1770
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 9.064e-33 9.521e-17
## Residual 9.918e-01 9.959e-01
## Number of obs: 18408, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.953e-03 1.315e-02 1.841e+04 0.605 0.545
## turnOverall:becomeFriends -1.749e-05 2.400e-05 1.841e+04 -0.729 0.466
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.830
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52094 52125 -26043 52086
## m1 4 52095 52126 -26043 52087 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m2: f0sdz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52094 52125 -26043 52086
## m2 4 52089 52120 -26041 52081 4.9691 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m3: f0sdz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52094 52125 -26043 52086
## m3 4 52094 52125 -26043 52086 0.1662 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: ipus
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m4: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 52094 52125 -26043 52086
## m4 4 52094 52126 -26043 52086 0 0# lists
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10427.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6763 -0.6572 -0.0888 0.6277 3.6239
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1777 0.4215
## Residual 0.8119 0.9010
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.657e-02 7.238e-02 4.209e+01 1.334 0.189
## turnOverall -6.234e-03 1.178e-03 3.913e+03 -5.291 1.28e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.249summary(m1 <- lmer(f0sdz ~ turnOverall : closeness + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:closeness + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10447.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6342 -0.6530 -0.0904 0.6175 3.5981
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1733 0.4163
## Residual 0.8157 0.9031
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.523e-02 7.122e-02 4.127e+01 0.775 0.44251
## turnOverall:closeness -9.427e-04 2.881e-04 3.788e+03 -3.273 0.00108 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.232summary(m2 <- lmer(f0sdz ~ turnOverall : similarity + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:similarity + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10430.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7049 -0.6482 -0.0863 0.6338 3.6281
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1820 0.4266
## Residual 0.8115 0.9008
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.553e-02 7.301e-02 4.161e+01 1.308 0.198
## turnOverall:similarity -1.090e-03 2.021e-04 3.888e+03 -5.395 7.26e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.240summary(m3 <- lmer(f0sdz ~ turnOverall : likeability + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:likeability + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10436.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6656 -0.6535 -0.0748 0.6216 3.6164
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1768 0.4205
## Residual 0.8129 0.9016
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.803e-02 7.223e-02 4.212e+01 1.219 0.23
## turnOverall:likeability -8.289e-04 1.726e-04 3.905e+03 -4.803 1.62e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.250summary(m4 <- lmer(f0sdz ~ turnOverall : becomeFriends + (1|speaker), ipus |> filter(task=="Lists")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: filter(ipus, task == "Lists")
##
## REML criterion at convergence: 10431.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6920 -0.6552 -0.0768 0.6144 3.6217
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.1764 0.420
## Residual 0.8119 0.901
## Number of obs: 3916, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.341e-02 7.197e-02 4.168e+01 1.298 0.201
## turnOverall:becomeFriends -1.006e-03 1.887e-04 3.889e+03 -5.329 1.04e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.241anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10421 10446 -5206.4 10413
## m1 4 10438 10463 -5214.9 10430 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m2: f0sdz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10421 10446 -5206.4 10413
## m2 4 10420 10445 -5205.8 10412 1.0541 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m3: f0sdz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10421 10446 -5206.4 10413
## m3 4 10426 10451 -5208.8 10418 0 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Lists")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m4: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 10421 10446 -5206.4 10413
## m4 4 10420 10445 -5206.2 10412 0.4093 0# diapix
summary(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40934.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3765 -0.7068 -0.0697 0.6413 5.1812
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01269 0.1127
## Residual 0.98114 0.9905
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.401e-02 3.125e-02 1.713e+02 2.048 0.04208 *
## turnOverall -6.827e-04 2.553e-04 1.036e+04 -2.674 0.00752 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## turnOverall -0.754summary(m1 <- lmer(f0sdz ~ turnOverall : closeness + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:closeness + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40940.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4000 -0.7074 -0.0682 0.6407 5.1852
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01393 0.1180
## Residual 0.98115 0.9905
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.808e-02 2.770e-02 7.245e+01 1.375 0.1735
## turnOverall:closeness -1.091e-04 5.186e-05 7.115e+02 -2.104 0.0357 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:cl -0.637summary(m2 <- lmer(f0sdz ~ turnOverall : similarity + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:similarity + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40928.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3818 -0.7086 -0.0712 0.6429 5.1714
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01275 0.1129
## Residual 0.98047 0.9902
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.786e-02 2.949e-02 1.160e+02 2.979 0.00352 **
## turnOverall:similarity -1.690e-04 4.113e-05 2.189e+03 -4.110 4.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:sm -0.717summary(m3 <- lmer(f0sdz ~ turnOverall : likeability + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:likeability + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40936
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3852 -0.7065 -0.0678 0.6435 5.1781
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01314 0.1146
## Residual 0.98089 0.9904
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.133e-02 3.056e-02 1.385e+02 2.334 0.02104 *
## turnOverall:likeability -1.128e-04 3.586e-05 5.352e+03 -3.146 0.00166 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:lk -0.732summary(m4 <- lmer(f0sdz ~ turnOverall : becomeFriends + (1|speaker), ipus |> filter(task=="Diapix")))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## Data: filter(ipus, task == "Diapix")
##
## REML criterion at convergence: 40937.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3840 -0.7067 -0.0708 0.6432 5.1820
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.01354 0.1164
## Residual 0.98094 0.9904
## Number of obs: 14492, groups: speaker, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.210e-02 2.996e-02 1.107e+02 2.073 0.0405 *
## turnOverall:becomeFriends -1.130e-04 3.931e-05 2.089e+03 -2.873 0.0041 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trnOvrll:bF -0.710anova(m, m1)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m1: f0sdz ~ turnOverall:closeness + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40922 40952 -20457 40914
## m1 4 40925 40955 -20459 40917 0 0anova(m, m2)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m2: f0sdz ~ turnOverall:similarity + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40922 40952 -20457 40914
## m2 4 40912 40943 -20452 40904 9.7319 0anova(m, m3)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m3: f0sdz ~ turnOverall:likeability + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40922 40952 -20457 40914
## m3 4 40919 40950 -20456 40911 2.6889 0anova(m, m4)## refitting model(s) with ML (instead of REML)## Data: filter(ipus, task == "Diapix")
## Models:
## m: f0sdz ~ turnOverall + (1 | speaker)
## m4: f0sdz ~ turnOverall:becomeFriends + (1 | speaker)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m 4 40922 40952 -20457 40914
## m4 4 40921 40951 -20457 40913 0.978 0Temperature (numerical) affecting f0
tos <- data.frame(matrix(nrow=0, ncol=5))
names(tos) <- c("f0feature", "ROI", "section", "direction", "temperatureAffectsF0")
for(r in unique(ipus$ROI)){
# f0 median
c <- broom.mixed::tidy(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeEntireExp), ROI==r)))
c1 <- broom.mixed::tidy(m1 <- lmer(f0medz ~ turnOverall * tempChangeEntireExp + (1|speaker), ipus |> filter(!is.na(tempChangeEntireExp), ROI==r)))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0median", r, "entireExp",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
if(nrow(ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")) > 0){
c <- broom.mixed::tidy(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")))
c1 <- broom.mixed::tidy(m1 <- lmer(f0medz ~ turnOverall * tempChangeLists + (1|speaker), ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0median", r, "Lists",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
}
c <- broom.mixed::tidy(m <- lmer(f0medz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeDiapix), ROI==r, task=="Diapix")))
c1 <- broom.mixed::tidy(m1 <- lmer(f0medz ~ turnOverall * tempChangeDiapix + (1|speaker), ipus |> filter(!is.na(tempChangeDiapix), ROI==r, task=="Diapix")))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0median", r, "Diapix",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
# f0 max
c <- broom.mixed::tidy(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeEntireExp), ROI==r)))
c1 <- broom.mixed::tidy(m1 <- lmer(f0maxz ~ turnOverall * tempChangeEntireExp + (1|speaker), ipus |> filter(!is.na(tempChangeEntireExp), ROI==r)))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0max", r, "entireExp",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
if(nrow(ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")) > 0){
c <- broom.mixed::tidy(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")))
c1 <- broom.mixed::tidy(m1 <- lmer(f0maxz ~ turnOverall * tempChangeLists + (1|speaker), ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0max", r, "Lists",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
}
c <- broom.mixed::tidy(m <- lmer(f0maxz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeDiapix), ROI==r, task=="Diapix")))
c1 <- broom.mixed::tidy(m1 <- lmer(f0maxz ~ turnOverall * tempChangeDiapix + (1|speaker), ipus |> filter(!is.na(tempChangeDiapix), ROI==r, task=="Diapix")))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0max", r, "Diapix",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
# f0 SD
c <- broom.mixed::tidy(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeEntireExp), ROI==r)))
c1 <- broom.mixed::tidy(m1 <- lmer(f0sdz ~ turnOverall * tempChangeEntireExp + (1|speaker), ipus |> filter(!is.na(tempChangeEntireExp), ROI==r)))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0sd", r, "entireExp",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
if(nrow(ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")) > 0){
c <- broom.mixed::tidy(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")))
c1 <- broom.mixed::tidy(m1 <- lmer(f0sdz ~ turnOverall * tempChangeLists + (1|speaker), ipus |> filter(!is.na(tempChangeLists), ROI==r, task=="Lists")))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0sd", r, "Lists",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
}
c <- broom.mixed::tidy(m <- lmer(f0sdz ~ turnOverall + (1|speaker), ipus |> filter(!is.na(tempChangeDiapix), ROI==r, task=="Diapix")))
c1 <- broom.mixed::tidy(m1 <- lmer(f0sdz ~ turnOverall * tempChangeDiapix + (1|speaker), ipus |> filter(!is.na(tempChangeDiapix), ROI==r, task=="Diapix")))
a <- anova(m, m1)
tos[nrow(tos)+1,] <- c("f0sd", r, "Diapix",
ifelse(c1$estimate[4] < 0, "negative", "positive"),
ifelse(a$AIC[2] - a$AIC[1] <= -2 & c1$p.value[4] < 0.05, "affects", "doesnt"))
}## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce q## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 3 from bobyqa: bobyqa -- a trust region step failed to
## reduce q## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)print("These are the significant effects of temperature on f0 (per ROI and f0 feature):")## [1] "These are the significant effects of temperature on f0 (per ROI and f0 feature):"print(tos |> filter(temperatureAffectsF0=="affects") |> arrange(section))## f0feature ROI section direction temperatureAffectsF0
## 1 f0sd Eyes entireExp negative affects
## 2 f0median Nose entireExp negative affects
## 3 f0median Cheeks entireExp negative affects
## 4 f0max Cheeks entireExp negative affects
## 5 f0sd Cheeks entireExp negative affectsLocal convergence
(following Levitan & Hirschberg 2011’s method)
load(paste0(here::here(), "/data/speechData.RData"))
all <- data.frame(matrix(nrow=0, ncol=9))
names(all) <- c("speaker", "type", "direction", "coefficient", "mock", "real", "condition", "feature", "section")f0 median
Across the entire experiment
ggplot(dat, aes(turnOverall, f0medDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 430 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 430 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s)
c <- cor.test(d$f0medDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMedDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0median",
section = "entireExp",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only 3 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One converged and two diverged on f0 median. All from the impersonal condition.
print(count)## speaker type direction coefficient mock real condition
## 1 HAG-A convergence divergence 0.0585051544624926 0 no close
## 2 HBR-A convergence convergence -0.0251212645486692 0 no impersonal
## 3 HUJ-B convergence convergence -0.00750409423012982 0 no close
## 4 KPB-A convergence convergence -0.0354923347870457 0 no close
## 5 KPB-B convergence divergence 0.0191921570743888 0 no close
## 6 NLO-B convergence divergence 0.0345706069155182 0 no close
## 7 OAL-B convergence convergence -0.124834077506019 0 no impersonal
## 8 OXQ-B convergence divergence 0.166665816388928 0 yes impersonal
## 9 QRT-B convergence convergence -0.0836923992008052 0 no close
## 10 SGB-A convergence convergence -0.0219420233127176 0 no impersonal
## 11 SUK-A convergence convergence -0.0393606102048184 0 no impersonal
## 12 SUK-B convergence convergence -0.131940400166196 0 no impersonal
## 13 TTN-B convergence convergence -0.101531413290794 0 no close
## 14 TTY-A convergence divergence 0.0580479706097723 0 no close
## 15 VDE-B convergence divergence 0.00622333715956697 0 no impersonal
## 16 FWR-A convergence divergence 0.19142983203941 1 no close
## 17 HBR-B convergence divergence 0.189733700626366 1 yes impersonal
## 18 KDA-B convergence divergence 0.0999034595944067 1 no impersonal
## 19 OXQ-A convergence convergence -0.0666762187913525 1 no impersonal
## 20 QRT-A convergence convergence -0.154659734620579 1 no close
## 21 SGB-B convergence convergence -0.163349472489718 1 no impersonal
## 22 TTN-A convergence divergence 0.0854815432161278 1 no close
## 23 TTY-B convergence convergence -0.105368664223288 1 no close
## 24 ZNV-B convergence divergence 0.0457135841426055 1 no close
## 25 FWR-B convergence convergence -0.448622846844869 2 no close
## 26 FXO-B convergence convergence -0.337329882306175 2 no impersonal
## 27 KDA-A convergence divergence 0.0491004567553633 2 no impersonal
## 28 HAG-B convergence convergence -0.243303988703851 3 no close
## 29 HUJ-A convergence divergence 0.22341521128362 3 no close
## 30 OAL-A convergence convergence -0.122336636013981 3 no impersonal
## 31 AML-B convergence convergence -0.264839601839081 5 no impersonal
## 32 MJG-B convergence divergence 0.254146876437062 5 no close
## 33 NLO-A convergence divergence 0.251454148243944 5 no close
## 34 ZNV-A convergence convergence -0.191693549932247 5 no close
## 35 MJG-A convergence divergence 0.315877637701108 6 no close
## 36 FXO-A convergence convergence -0.371862250906732 7 no impersonal
## 37 AML-A convergence convergence -0.0530817180998742 8 no impersonal
## 38 VDE-A convergence divergence 0.14295996583943 10 no impersonal
## feature section
## 1 f0median entireExp
## 2 f0median entireExp
## 3 f0median entireExp
## 4 f0median entireExp
## 5 f0median entireExp
## 6 f0median entireExp
## 7 f0median entireExp
## 8 f0median entireExp
## 9 f0median entireExp
## 10 f0median entireExp
## 11 f0median entireExp
## 12 f0median entireExp
## 13 f0median entireExp
## 14 f0median entireExp
## 15 f0median entireExp
## 16 f0median entireExp
## 17 f0median entireExp
## 18 f0median entireExp
## 19 f0median entireExp
## 20 f0median entireExp
## 21 f0median entireExp
## 22 f0median entireExp
## 23 f0median entireExp
## 24 f0median entireExp
## 25 f0median entireExp
## 26 f0median entireExp
## 27 f0median entireExp
## 28 f0median entireExp
## 29 f0median entireExp
## 30 f0median entireExp
## 31 f0median entireExp
## 32 f0median entireExp
## 33 f0median entireExp
## 34 f0median entireExp
## 35 f0median entireExp
## 36 f0median entireExp
## 37 f0median entireExp
## 38 f0median entireExpOnly in the first part of the experiment
Since we could argue that the speakers converge to each other only in the beginning, and that in the second part of the experiment the difference between f0s doesn’t keep decreasing (or increasing)
ggplot(dat |> filter(task=="Lists"), aes(turnOverall, f0medDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 133 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 133 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Lists")
c <- cor.test(d$f0medDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMedDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0median",
section = "Lists",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only 2 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One converged (in the close condition) and one diverged (impersonal condition) on f0 median. These 2 are different from the 3 displayed above.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A convergence divergence 0.143000566597349 0 no impersonal
## 2 FXO-A convergence divergence 0.107264785824985 0 no impersonal
## 3 FXO-B convergence divergence 0.154868071811506 0 no impersonal
## 4 HAG-B convergence divergence 0.147333240890105 0 no close
## 5 HBR-A convergence convergence -0.0538049458431317 0 no impersonal
## 6 HUJ-B convergence divergence 0.0250877420306285 0 no close
## 7 KDA-A convergence convergence -0.0714128403969355 0 no impersonal
## 8 KDA-B convergence divergence 0.0665630238984155 0 no impersonal
## 9 KPB-A convergence divergence 0.455765691195585 0 no close
## 10 MJG-A convergence convergence -0.0763107248583225 0 no close
## 11 MJG-B convergence divergence 0.161956129858464 0 no close
## 12 NLO-A convergence convergence -0.231340378328207 0 no close
## 13 OAL-A convergence divergence 0.203285282194069 0 no impersonal
## 14 OAL-B convergence convergence -0.106492016692283 0 no impersonal
## 15 OXQ-A convergence convergence -0.125378327692747 0 no impersonal
## 16 TTN-B convergence convergence -0.163205163334084 0 no close
## 17 TTY-B convergence divergence 0.100533526945519 0 no close
## 18 VDE-A convergence divergence 0.0556163450077026 0 no impersonal
## 19 ZNV-B convergence divergence 0.0561102812902435 0 no close
## 20 AML-B convergence convergence -0.289261443218117 1 no impersonal
## 21 FWR-A convergence divergence 0.19142983203941 1 no close
## 22 HAG-A convergence convergence -0.00197330763268993 1 no close
## 23 KPB-B convergence convergence -0.348824035411028 1 no close
## 24 NLO-B convergence convergence -0.215678209395366 1 no close
## 25 OXQ-B convergence divergence 0.181828667052807 1 no impersonal
## 26 QRT-A convergence divergence 0.192868891385498 1 no close
## 27 QRT-B convergence convergence -0.253776399953452 1 no close
## 28 SGB-B convergence convergence -0.0311039296351395 1 no impersonal
## 29 TTY-A convergence convergence -0.130624378856547 1 no close
## 30 ZNV-A convergence convergence -0.144478973054806 1 no close
## 31 FWR-B convergence convergence -0.448622846844869 2 no close
## 32 HUJ-A convergence divergence 0.500424842133659 2 no close
## 33 SUK-A convergence divergence 0.258140370359425 2 no impersonal
## 34 VDE-B convergence convergence -0.436894436925545 2 no impersonal
## 35 SUK-B convergence divergence 0.290701759954789 3 no impersonal
## 36 TTN-A convergence divergence 0.369258867205108 3 no close
## 37 HBR-B convergence divergence 0.0183314479061065 4 no impersonal
## 38 SGB-A convergence divergence 0.325654821030138 6 no impersonal
## feature section
## 1 f0median Lists
## 2 f0median Lists
## 3 f0median Lists
## 4 f0median Lists
## 5 f0median Lists
## 6 f0median Lists
## 7 f0median Lists
## 8 f0median Lists
## 9 f0median Lists
## 10 f0median Lists
## 11 f0median Lists
## 12 f0median Lists
## 13 f0median Lists
## 14 f0median Lists
## 15 f0median Lists
## 16 f0median Lists
## 17 f0median Lists
## 18 f0median Lists
## 19 f0median Lists
## 20 f0median Lists
## 21 f0median Lists
## 22 f0median Lists
## 23 f0median Lists
## 24 f0median Lists
## 25 f0median Lists
## 26 f0median Lists
## 27 f0median Lists
## 28 f0median Lists
## 29 f0median Lists
## 30 f0median Lists
## 31 f0median Lists
## 32 f0median Lists
## 33 f0median Lists
## 34 f0median Lists
## 35 f0median Lists
## 36 f0median Lists
## 37 f0median Lists
## 38 f0median ListsOnly in the second part of the experiment
Since we could argue that the speakers converge to each other more in the second part of the experiment, after they already got to know each other (and like each other or not) in the first part.
ggplot(dat |> filter(task=="Diapix"), aes(turnOverall, f0medDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 297 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 297 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Diapix")
if(nrow(d) ==0){next} # because we don't have the Diapix files for one dyad
c <- cor.test(d$f0medDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMedDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0median",
section = "Diapix",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only speaker whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
She was in the close condition and converged to her partner.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A convergence convergence -0.116165929038044 0 no impersonal
## 2 FXO-B convergence convergence -0.00255870671399689 0 no impersonal
## 3 HBR-A convergence divergence 0.00496831405175861 0 no impersonal
## 4 KPB-A convergence convergence -0.0304236372880813 0 no close
## 5 MJG-B convergence divergence 0.179855964890547 0 no close
## 6 NLO-B convergence convergence -0.0632223762383439 0 no close
## 7 OAL-A convergence convergence -0.0568871709273923 0 no impersonal
## 8 OAL-B convergence divergence 0.0895022962542955 0 no impersonal
## 9 OXQ-A convergence divergence 0.0477440405746218 0 no impersonal
## 10 OXQ-B convergence divergence 0.069221210908606 0 no impersonal
## 11 QRT-B convergence convergence -0.105297705492937 0 no close
## 12 SGB-A convergence divergence 0.137721634682491 0 no impersonal
## 13 SGB-B convergence divergence 0.0492801672329371 0 no impersonal
## 14 SUK-A convergence convergence -0.0110708338044713 0 no impersonal
## 15 SUK-B convergence convergence -0.125985004560869 0 no impersonal
## 16 TTY-B convergence convergence -0.220872291596236 0 yes close
## 17 VDE-B convergence convergence -0.0254190662169023 0 no impersonal
## 18 AML-B convergence convergence -0.0827303128312628 1 no impersonal
## 19 FXO-A convergence convergence -0.043178214859707 1 no impersonal
## 20 HAG-A convergence convergence -0.139158951489129 1 no close
## 21 KDA-A convergence convergence -0.00326332941577587 1 no impersonal
## 22 KDA-B convergence divergence 0.000126012247539085 1 no impersonal
## 23 KPB-B convergence divergence 0.0427356209487656 1 no close
## 24 NLO-A convergence divergence 0.108265693725868 1 no close
## 25 TTN-A convergence divergence 0.0716855006099233 1 no close
## 26 TTN-B convergence convergence -0.124610176761358 1 no close
## 27 ZNV-A convergence divergence 0.127753701807482 1 no close
## 28 HBR-B convergence divergence 0.0681380809942917 2 no impersonal
## 29 QRT-A convergence convergence -0.0121899842183128 2 no close
## 30 VDE-A convergence divergence 0.0108995823759431 2 no impersonal
## 31 HAG-B convergence convergence -0.169148789632483 4 no close
## 32 HUJ-A convergence divergence 0.143208325542347 4 no close
## 33 HUJ-B convergence divergence 0.215219155138621 6 no close
## 34 TTY-A convergence convergence -0.155938155105248 6 no close
## 35 ZNV-B convergence divergence 0.0878094623727449 6 no close
## 36 MJG-A convergence divergence 0.215852904124754 10 no close
## feature section
## 1 f0median Diapix
## 2 f0median Diapix
## 3 f0median Diapix
## 4 f0median Diapix
## 5 f0median Diapix
## 6 f0median Diapix
## 7 f0median Diapix
## 8 f0median Diapix
## 9 f0median Diapix
## 10 f0median Diapix
## 11 f0median Diapix
## 12 f0median Diapix
## 13 f0median Diapix
## 14 f0median Diapix
## 15 f0median Diapix
## 16 f0median Diapix
## 17 f0median Diapix
## 18 f0median Diapix
## 19 f0median Diapix
## 20 f0median Diapix
## 21 f0median Diapix
## 22 f0median Diapix
## 23 f0median Diapix
## 24 f0median Diapix
## 25 f0median Diapix
## 26 f0median Diapix
## 27 f0median Diapix
## 28 f0median Diapix
## 29 f0median Diapix
## 30 f0median Diapix
## 31 f0median Diapix
## 32 f0median Diapix
## 33 f0median Diapix
## 34 f0median Diapix
## 35 f0median Diapix
## 36 f0median Diapixf0 max
Across the entire experiment
ggplot(dat, aes(turnOverall, f0maxDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 288 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 288 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s)
c <- cor.test(d$f0maxDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMaxDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0max",
section = "entireExp",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)No one seemed to converge or diverge on f0 max.
print(count)## speaker type direction coefficient mock real condition
## 1 FWR-A convergence divergence 0.195053915574296 0 no close
## 2 HAG-A convergence divergence 0.0435974260241883 0 no close
## 3 HBR-A convergence divergence 0.0852160213942602 0 no impersonal
## 4 HUJ-A convergence divergence 0.0264816729442473 0 no close
## 5 HUJ-B convergence convergence -0.0088900092639663 0 no close
## 6 KDA-A convergence convergence -0.0141080708683843 0 no impersonal
## 7 KDA-B convergence divergence 0.0298363008427167 0 no impersonal
## 8 KPB-B convergence convergence -0.0185706119535337 0 no close
## 9 MJG-A convergence convergence -0.0523415243973334 0 no close
## 10 NLO-B convergence divergence 0.0837320474903288 0 no close
## 11 QRT-A convergence divergence 0.011549552899041 0 no close
## 12 SGB-B convergence convergence -0.0842949435657642 0 no impersonal
## 13 SUK-A convergence divergence 0.0523540536536774 0 no impersonal
## 14 SUK-B convergence convergence -0.125408861712307 0 no impersonal
## 15 TTY-B convergence convergence -0.125693469234968 0 no close
## 16 VDE-B convergence divergence 0.0729246516544962 0 no impersonal
## 17 AML-B convergence divergence 0.103808291542023 1 no impersonal
## 18 FWR-B convergence convergence -0.134951173385048 1 no close
## 19 HBR-B convergence divergence 0.0621035498940902 1 no impersonal
## 20 MJG-B convergence convergence -0.0651902486689506 1 no close
## 21 OXQ-A convergence convergence -0.0847541361802634 1 no impersonal
## 22 QRT-B convergence divergence 0.0997800705740454 1 no close
## 23 TTN-A convergence convergence -0.0251091331120037 1 no close
## 24 AML-A convergence divergence 0.128806264299608 2 no impersonal
## 25 FXO-A convergence convergence -0.130250361429113 2 no impersonal
## 26 HAG-B convergence convergence -0.00264982355773621 2 no close
## 27 KPB-A convergence divergence 0.174598112103041 2 no close
## 28 OAL-A convergence convergence -0.118887343437888 2 no impersonal
## 29 OXQ-B convergence convergence -0.0866413306656346 2 no impersonal
## 30 TTN-B convergence convergence -0.0930170531150306 2 no close
## 31 TTY-A convergence divergence 0.0941140076253447 2 no close
## 32 ZNV-B convergence convergence -0.141102143040279 2 no close
## 33 NLO-A convergence convergence -0.0262142453422706 3 no close
## 34 SGB-A convergence divergence 0.0709269411109755 3 no impersonal
## 35 VDE-A convergence divergence 0.250965956882606 5 no impersonal
## 36 OAL-B convergence convergence -0.166686346228455 6 no impersonal
## 37 ZNV-A convergence convergence -0.280203241209376 6 no close
## 38 FXO-B convergence convergence -0.0551698498902035 7 no impersonal
## feature section
## 1 f0max entireExp
## 2 f0max entireExp
## 3 f0max entireExp
## 4 f0max entireExp
## 5 f0max entireExp
## 6 f0max entireExp
## 7 f0max entireExp
## 8 f0max entireExp
## 9 f0max entireExp
## 10 f0max entireExp
## 11 f0max entireExp
## 12 f0max entireExp
## 13 f0max entireExp
## 14 f0max entireExp
## 15 f0max entireExp
## 16 f0max entireExp
## 17 f0max entireExp
## 18 f0max entireExp
## 19 f0max entireExp
## 20 f0max entireExp
## 21 f0max entireExp
## 22 f0max entireExp
## 23 f0max entireExp
## 24 f0max entireExp
## 25 f0max entireExp
## 26 f0max entireExp
## 27 f0max entireExp
## 28 f0max entireExp
## 29 f0max entireExp
## 30 f0max entireExp
## 31 f0max entireExp
## 32 f0max entireExp
## 33 f0max entireExp
## 34 f0max entireExp
## 35 f0max entireExp
## 36 f0max entireExp
## 37 f0max entireExp
## 38 f0max entireExpOnly in the first part of the experiment
Since we could argue that the speakers converge to each other only in the beginning, and that in the second part of the experiment the difference between f0s doesn’t keep decreasing (or increasing)
ggplot(dat |> filter(task=="Lists"), aes(turnOverall, f0maxDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 97 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 97 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Lists")
c <- cor.test(d$f0maxDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMaxDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0max",
section = "Lists",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only 2 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One converged (in the close condition) and one diverged (impersonal condition) on f0 max.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A convergence convergence -0.0626303610955421 0 no impersonal
## 2 AML-B convergence convergence -0.137472448004817 0 no impersonal
## 3 FWR-A convergence divergence 0.195053915574296 0 no close
## 4 HAG-A convergence convergence -0.124051226935211 0 no close
## 5 KDA-A convergence divergence 0.0889225609819929 0 no impersonal
## 6 NLO-A convergence convergence -0.132572372228093 0 no close
## 7 NLO-B convergence convergence -0.443954182939341 0 yes close
## 8 OAL-A convergence convergence -0.162915287248881 0 no impersonal
## 9 OXQ-B convergence divergence 0.104661144667901 0 no impersonal
## 10 QRT-A convergence divergence 0.147478402879362 0 no close
## 11 QRT-B convergence divergence 0.125567025914835 0 no close
## 12 SGB-A convergence convergence -0.0839756904741547 0 no impersonal
## 13 SUK-B convergence divergence 0.291699724936196 0 no impersonal
## 14 TTY-B convergence divergence 0.16839895147975 0 no close
## 15 VDE-A convergence convergence -0.0631385635184396 0 no impersonal
## 16 VDE-B convergence convergence -0.276028480202935 0 yes impersonal
## 17 ZNV-B convergence divergence 0.0736673095224291 0 no close
## 18 FWR-B convergence convergence -0.134951173385048 1 no close
## 19 HAG-B convergence convergence -0.189245055548581 1 no close
## 20 KPB-B convergence divergence 0.503872552561909 1 no close
## 21 MJG-A convergence convergence -0.187920194544154 1 no close
## 22 MJG-B convergence convergence -0.18280788432291 1 no close
## 23 OXQ-A convergence convergence -0.085880364487863 1 no impersonal
## 24 SUK-A convergence divergence 0.0631106131160197 1 no impersonal
## 25 TTN-A convergence convergence -0.0786115569458072 1 no close
## 26 HBR-A convergence convergence -0.0641652582689635 2 no impersonal
## 27 HUJ-A convergence divergence 0.137300342658815 2 no close
## 28 KDA-B convergence divergence 0.0236434627523229 2 no impersonal
## 29 OAL-B convergence convergence -0.11120762451653 2 no impersonal
## 30 TTN-B convergence divergence 0.244375632518684 2 no close
## 31 TTY-A convergence divergence 0.18222840071088 2 no close
## 32 ZNV-A convergence divergence 0.0353426933221924 2 no close
## 33 FXO-B convergence convergence -0.197644259218367 3 no impersonal
## 34 KPB-A convergence divergence 0.561528733407216 3 no close
## 35 SGB-B convergence convergence -0.0211870198904643 3 no impersonal
## 36 HBR-B convergence convergence -0.304817762472401 4 no impersonal
## 37 FXO-A convergence divergence 0.0514575241315975 5 no impersonal
## 38 HUJ-B convergence divergence 0.229943553107705 8 no close
## feature section
## 1 f0max Lists
## 2 f0max Lists
## 3 f0max Lists
## 4 f0max Lists
## 5 f0max Lists
## 6 f0max Lists
## 7 f0max Lists
## 8 f0max Lists
## 9 f0max Lists
## 10 f0max Lists
## 11 f0max Lists
## 12 f0max Lists
## 13 f0max Lists
## 14 f0max Lists
## 15 f0max Lists
## 16 f0max Lists
## 17 f0max Lists
## 18 f0max Lists
## 19 f0max Lists
## 20 f0max Lists
## 21 f0max Lists
## 22 f0max Lists
## 23 f0max Lists
## 24 f0max Lists
## 25 f0max Lists
## 26 f0max Lists
## 27 f0max Lists
## 28 f0max Lists
## 29 f0max Lists
## 30 f0max Lists
## 31 f0max Lists
## 32 f0max Lists
## 33 f0max Lists
## 34 f0max Lists
## 35 f0max Lists
## 36 f0max Lists
## 37 f0max Lists
## 38 f0max ListsOnly in the second part of the experiment
Since we could argue that the speakers converge to each other more in the second part of the experiment, after they already got to know each other (and like each other or not) in the first part.
ggplot(dat |> filter(task=="Diapix"), aes(turnOverall, f0maxDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 191 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 191 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Diapix")
if(nrow(d) ==0){next} # because we don't have the Diapix files for one dyad
c <- cor.test(d$f0maxDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMaxDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0max",
section = "Diapix",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only 2 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One diverged and one converged on f0 max. Both were in the impersonal condition.
print(count)## speaker type direction coefficient mock real condition
## 1 FXO-B convergence divergence 0.0974122952796964 0 no impersonal
## 2 HAG-A convergence divergence 0.0325481436955988 0 no close
## 3 HBR-A convergence divergence 0.215356401552828 0 no impersonal
## 4 KDA-A convergence convergence -0.195059068504469 0 no impersonal
## 5 KDA-B convergence convergence -0.170630867010071 0 no impersonal
## 6 KPB-B convergence divergence 0.0704765917566594 0 no close
## 7 MJG-B convergence convergence -0.116372109132848 0 no close
## 8 NLO-B convergence divergence 0.0460927768827829 0 no close
## 9 OXQ-B convergence convergence -0.0133341764805421 0 no impersonal
## 10 QRT-A convergence divergence 0.0779197276208909 0 no close
## 11 QRT-B convergence divergence 0.102040437968312 0 no close
## 12 SGB-A convergence divergence 0.0270359347073647 0 no impersonal
## 13 SUK-B convergence convergence -0.148783229602822 0 no impersonal
## 14 TTN-A convergence convergence -0.163852741608531 0 no close
## 15 TTY-B convergence convergence -0.147841922211197 0 no close
## 16 VDE-A convergence divergence 0.153119485091093 0 no impersonal
## 17 ZNV-A convergence divergence 0.0536471338734851 0 no close
## 18 ZNV-B convergence convergence -0.00864303754645299 0 no close
## 19 AML-A convergence divergence 0.146730552290373 1 no impersonal
## 20 FXO-A convergence convergence -0.0176680668070729 1 no impersonal
## 21 HAG-B convergence convergence -0.0580246050309827 1 no close
## 22 KPB-A convergence divergence 0.117358733371286 1 no close
## 23 MJG-A convergence convergence -0.145981119344687 1 no close
## 24 NLO-A convergence divergence 0.0165806333009691 1 no close
## 25 SGB-B convergence convergence -0.17679358314786 1 no impersonal
## 26 VDE-B convergence divergence 0.122539500552027 1 no impersonal
## 27 HUJ-A convergence divergence 0.11804686302089 2 no close
## 28 OAL-A convergence convergence -0.238229191413814 2 no impersonal
## 29 SUK-A convergence divergence 0.0571407238100443 2 no impersonal
## 30 AML-B convergence divergence 0.0985094506287954 3 no impersonal
## 31 HBR-B convergence divergence 0.220937824398609 3 no impersonal
## 32 HUJ-B convergence divergence 0.104582246445074 3 no close
## 33 OAL-B convergence convergence -0.167341105719336 3 no impersonal
## 34 TTN-B convergence convergence -0.148078253160875 3 no close
## 35 OXQ-A convergence divergence 0.0216457593161531 4 no impersonal
## 36 TTY-A convergence divergence 0.0150163535059419 5 no close
## feature section
## 1 f0max Diapix
## 2 f0max Diapix
## 3 f0max Diapix
## 4 f0max Diapix
## 5 f0max Diapix
## 6 f0max Diapix
## 7 f0max Diapix
## 8 f0max Diapix
## 9 f0max Diapix
## 10 f0max Diapix
## 11 f0max Diapix
## 12 f0max Diapix
## 13 f0max Diapix
## 14 f0max Diapix
## 15 f0max Diapix
## 16 f0max Diapix
## 17 f0max Diapix
## 18 f0max Diapix
## 19 f0max Diapix
## 20 f0max Diapix
## 21 f0max Diapix
## 22 f0max Diapix
## 23 f0max Diapix
## 24 f0max Diapix
## 25 f0max Diapix
## 26 f0max Diapix
## 27 f0max Diapix
## 28 f0max Diapix
## 29 f0max Diapix
## 30 f0max Diapix
## 31 f0max Diapix
## 32 f0max Diapix
## 33 f0max Diapix
## 34 f0max Diapix
## 35 f0max Diapix
## 36 f0max Diapixf0 SD
Across the entire experiment
ggplot(dat, aes(turnOverall, f0sdDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 575 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 575 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s)
c <- cor.test(d$f0sdDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockSdDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0sd",
section = "entireExp",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)No one seemed to converge or diverge on f0 sd.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A convergence divergence 0.0214347198164549 0 no impersonal
## 2 FWR-A convergence divergence 0.381410831855009 0 no close
## 3 HAG-A convergence convergence -0.111388711938931 0 no close
## 4 HBR-A convergence divergence 0.11055116508764 0 no impersonal
## 5 HUJ-A convergence divergence 0.112514218269034 0 no close
## 6 MJG-A convergence divergence 0.108364718505304 0 no close
## 7 OAL-A convergence convergence -0.298741868255242 0 yes impersonal
## 8 OXQ-A convergence divergence 0.0883732843677492 0 no impersonal
## 9 OXQ-B convergence divergence 0.129968075164485 0 no impersonal
## 10 SGB-B convergence convergence -0.017808362944551 0 no impersonal
## 11 SUK-B convergence divergence 0.0815035913537748 0 no impersonal
## 12 TTN-A convergence divergence 0.0305506349296566 0 no close
## 13 TTN-B convergence convergence -0.0310049868726606 0 no close
## 14 ZNV-B convergence convergence -0.201336693011536 0 yes close
## 15 AML-B convergence convergence -0.000225128971050298 1 no impersonal
## 16 FWR-B convergence divergence 0.0507325591874094 1 no close
## 17 HAG-B convergence convergence -0.0199481020245841 1 no close
## 18 HBR-B convergence convergence -0.0904705999350023 1 no impersonal
## 19 HUJ-B convergence divergence 0.156278049333046 1 no close
## 20 SGB-A convergence divergence 0.0110069881251993 1 no impersonal
## 21 SUK-A convergence divergence 0.0145140989267818 1 no impersonal
## 22 TTY-A convergence convergence -0.0459941236150303 1 no close
## 23 TTY-B convergence convergence -0.115572975791875 1 no close
## 24 FXO-B convergence convergence -0.0176871120807929 2 no impersonal
## 25 KDA-A convergence convergence -0.0612453321167161 2 no impersonal
## 26 KDA-B convergence divergence 0.139130627674164 2 no impersonal
## 27 KPB-B convergence divergence 0.0670924000700283 2 no close
## 28 NLO-B convergence divergence 0.134312383035443 2 no close
## 29 ZNV-A convergence convergence -0.217826307627929 2 no close
## 30 FXO-A convergence convergence -0.254255544069265 3 no impersonal
## 31 KPB-A convergence divergence 0.206230871992645 3 no close
## 32 MJG-B convergence divergence 0.142863154421377 3 no close
## 33 VDE-A convergence divergence 0.29847223421496 3 no impersonal
## 34 NLO-A convergence divergence 0.0546105591199303 4 no close
## 35 QRT-B convergence divergence 0.251210464706423 4 no close
## 36 QRT-A convergence divergence 0.0991090120655462 5 no close
## 37 OAL-B convergence convergence -0.197777896513274 7 no impersonal
## 38 VDE-B convergence divergence 0.262492005058125 8 no impersonal
## feature section
## 1 f0sd entireExp
## 2 f0sd entireExp
## 3 f0sd entireExp
## 4 f0sd entireExp
## 5 f0sd entireExp
## 6 f0sd entireExp
## 7 f0sd entireExp
## 8 f0sd entireExp
## 9 f0sd entireExp
## 10 f0sd entireExp
## 11 f0sd entireExp
## 12 f0sd entireExp
## 13 f0sd entireExp
## 14 f0sd entireExp
## 15 f0sd entireExp
## 16 f0sd entireExp
## 17 f0sd entireExp
## 18 f0sd entireExp
## 19 f0sd entireExp
## 20 f0sd entireExp
## 21 f0sd entireExp
## 22 f0sd entireExp
## 23 f0sd entireExp
## 24 f0sd entireExp
## 25 f0sd entireExp
## 26 f0sd entireExp
## 27 f0sd entireExp
## 28 f0sd entireExp
## 29 f0sd entireExp
## 30 f0sd entireExp
## 31 f0sd entireExp
## 32 f0sd entireExp
## 33 f0sd entireExp
## 34 f0sd entireExp
## 35 f0sd entireExp
## 36 f0sd entireExp
## 37 f0sd entireExp
## 38 f0sd entireExpOnly in the first part of the experiment
Since we could argue that the speakers converge to each other only in the beginning, and that in the second part of the experiment the difference between f0s doesn’t keep decreasing (or increasing)
ggplot(dat |> filter(task=="Lists"), aes(turnOverall, f0sdDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 152 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 152 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Lists")
c <- cor.test(d$f0sdDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockSdDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0sd",
section = "Lists",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only 2 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One converged (in the impersonal condition) and one diverged (close condition) on f0 sd.
print(count)## speaker type direction coefficient mock real condition
## 1 FWR-A convergence divergence 0.381410831855009 0 no close
## 2 FXO-A convergence divergence 0.0121891760211383 0 no impersonal
## 3 HAG-A convergence convergence -0.00757213709685002 0 no close
## 4 HAG-B convergence divergence 0.0187532193945991 0 no close
## 5 HBR-A convergence divergence 0.302663055942697 0 no impersonal
## 6 HBR-B convergence convergence -0.155623451845258 0 no impersonal
## 7 HUJ-A convergence divergence 0.240509690797476 0 no close
## 8 KDA-B convergence convergence -0.00978828650430149 0 no impersonal
## 9 KPB-B convergence divergence 0.145399904108498 0 no close
## 10 MJG-B convergence convergence -0.356490052874218 0 no close
## 11 NLO-A convergence convergence -0.108842531567252 0 no close
## 12 OAL-B convergence convergence -0.0734388442832312 0 no impersonal
## 13 OXQ-A convergence convergence -0.117064541273057 0 no impersonal
## 14 SGB-A convergence divergence 0.0808076859175227 0 no impersonal
## 15 TTN-A convergence convergence -0.0846889361583049 0 no close
## 16 TTN-B convergence divergence 0.0334423909758696 0 no close
## 17 VDE-A convergence convergence -0.31549012844638 0 yes impersonal
## 18 ZNV-A convergence convergence -0.211092977165392 0 no close
## 19 ZNV-B convergence convergence -0.143139035328182 0 no close
## 20 FWR-B convergence divergence 0.0507325591874094 1 no close
## 21 KDA-A convergence convergence -0.0896216568251073 1 no impersonal
## 22 KPB-A convergence divergence 0.408712196058522 1 no close
## 23 NLO-B convergence divergence 0.102060759285047 1 no close
## 24 SGB-B convergence divergence 0.438803836732088 1 yes impersonal
## 25 SUK-A convergence divergence 0.134359079209268 1 no impersonal
## 26 SUK-B convergence divergence 0.136570853070235 1 no impersonal
## 27 TTY-A convergence convergence -0.180984245316223 1 no close
## 28 TTY-B convergence divergence 0.358199677461476 1 no close
## 29 VDE-B convergence convergence -0.164884973273884 1 no impersonal
## 30 FXO-B convergence convergence -0.101221268984188 2 no impersonal
## 31 OAL-A convergence convergence -0.195543304328618 2 no impersonal
## 32 OXQ-B convergence divergence 0.0980841393836394 2 no impersonal
## 33 AML-A convergence divergence 0.210803292805898 3 no impersonal
## 34 MJG-A convergence convergence -0.331569089539631 3 no close
## 35 QRT-B convergence divergence 0.0511840573862018 3 no close
## 36 QRT-A convergence divergence 0.301527018343838 4 no close
## 37 HUJ-B convergence divergence 0.332677030997308 6 no close
## 38 AML-B convergence convergence -0.00841782023317876 8 no impersonal
## feature section
## 1 f0sd Lists
## 2 f0sd Lists
## 3 f0sd Lists
## 4 f0sd Lists
## 5 f0sd Lists
## 6 f0sd Lists
## 7 f0sd Lists
## 8 f0sd Lists
## 9 f0sd Lists
## 10 f0sd Lists
## 11 f0sd Lists
## 12 f0sd Lists
## 13 f0sd Lists
## 14 f0sd Lists
## 15 f0sd Lists
## 16 f0sd Lists
## 17 f0sd Lists
## 18 f0sd Lists
## 19 f0sd Lists
## 20 f0sd Lists
## 21 f0sd Lists
## 22 f0sd Lists
## 23 f0sd Lists
## 24 f0sd Lists
## 25 f0sd Lists
## 26 f0sd Lists
## 27 f0sd Lists
## 28 f0sd Lists
## 29 f0sd Lists
## 30 f0sd Lists
## 31 f0sd Lists
## 32 f0sd Lists
## 33 f0sd Lists
## 34 f0sd Lists
## 35 f0sd Lists
## 36 f0sd Lists
## 37 f0sd Lists
## 38 f0sd ListsOnly in the second part of the experiment
Since we could argue that the speakers converge to each other more in the second part of the experiment, after they already got to know each other (and like each other or not) in the first part.
ggplot(dat |> filter(task=="Diapix"), aes(turnOverall, f0sdDiff))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 423 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 423 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Diapix")
if(nrow(d) ==0){next} # because we don't have the Diapix files for one dyad
c <- cor.test(d$f0sdDiff, d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockSdDiff", i)], d$turnOverall,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "convergence",
r > 0 ~ "divergence"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"convergence",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0sd",
section = "Diapix",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only 2 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One converged (in the impersonal condition) and one diverged (close condition) on f0 sd.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A convergence divergence 0.0942197153003442 0 no impersonal
## 2 FXO-B convergence divergence 0.049219819131411 0 no impersonal
## 3 HAG-A convergence convergence -0.0760416222732127 0 no close
## 4 HAG-B convergence convergence -0.0184458828754394 0 no close
## 5 HBR-A convergence divergence 0.0936714058114991 0 no impersonal
## 6 HUJ-A convergence divergence 0.0751725996909468 0 no close
## 7 MJG-A convergence convergence -0.101819594058672 0 no close
## 8 NLO-A convergence divergence 0.000666481925621654 0 no close
## 9 NLO-B convergence divergence 0.124821781378392 0 no close
## 10 OAL-A convergence convergence -0.324655159586839 0 yes impersonal
## 11 OXQ-A convergence divergence 0.063287055218376 0 no impersonal
## 12 OXQ-B convergence divergence 0.0534343869841281 0 no impersonal
## 13 QRT-A convergence divergence 0.00950107671534853 0 no close
## 14 TTN-A convergence convergence -0.0449604005575976 0 no close
## 15 TTY-A convergence divergence 0.00536565759536223 0 no close
## 16 TTY-B convergence convergence -0.0676666445524121 0 no close
## 17 VDE-B convergence divergence 0.101687708260141 0 no impersonal
## 18 ZNV-B convergence convergence -0.0973549456622127 0 no close
## 19 AML-B convergence convergence -0.0214090614850532 1 no impersonal
## 20 FXO-A convergence convergence -0.166141472032452 1 no impersonal
## 21 HBR-B convergence convergence -0.0343083755393114 1 no impersonal
## 22 HUJ-B convergence divergence 0.0716311843561288 1 no close
## 23 KDA-A convergence convergence -0.17957608473935 1 no impersonal
## 24 KPB-A convergence divergence 0.138716143514441 1 no close
## 25 KPB-B convergence convergence -0.00697540446754079 1 no close
## 26 QRT-B convergence divergence 0.241788295665581 1 yes close
## 27 SGB-B convergence convergence -0.0846969224524156 1 no impersonal
## 28 SUK-A convergence divergence 0.0985858289170189 1 no impersonal
## 29 SUK-B convergence divergence 0.100079409496665 1 no impersonal
## 30 TTN-B convergence convergence -0.19308810492801 1 no close
## 31 ZNV-A convergence divergence 0.0474148512043777 1 no close
## 32 KDA-B convergence convergence -0.0354262936436638 2 no impersonal
## 33 MJG-B convergence divergence 0.0217175547285901 2 no close
## 34 SGB-A convergence convergence -0.0346656866245028 2 no impersonal
## 35 OAL-B convergence convergence -0.205558164852786 4 no impersonal
## 36 VDE-A convergence divergence 0.083010733276941 5 no impersonal
## feature section
## 1 f0sd Diapix
## 2 f0sd Diapix
## 3 f0sd Diapix
## 4 f0sd Diapix
## 5 f0sd Diapix
## 6 f0sd Diapix
## 7 f0sd Diapix
## 8 f0sd Diapix
## 9 f0sd Diapix
## 10 f0sd Diapix
## 11 f0sd Diapix
## 12 f0sd Diapix
## 13 f0sd Diapix
## 14 f0sd Diapix
## 15 f0sd Diapix
## 16 f0sd Diapix
## 17 f0sd Diapix
## 18 f0sd Diapix
## 19 f0sd Diapix
## 20 f0sd Diapix
## 21 f0sd Diapix
## 22 f0sd Diapix
## 23 f0sd Diapix
## 24 f0sd Diapix
## 25 f0sd Diapix
## 26 f0sd Diapix
## 27 f0sd Diapix
## 28 f0sd Diapix
## 29 f0sd Diapix
## 30 f0sd Diapix
## 31 f0sd Diapix
## 32 f0sd Diapix
## 33 f0sd Diapix
## 34 f0sd Diapix
## 35 f0sd Diapix
## 36 f0sd DiapixLocal Synchrony
f0 median
Across the entire experiment
ggplot(dat, aes(f0medzGender, prevf0med))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 430 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 430 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s)
c <- cor.test(d$f0medzGender, d$prevf0med,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMed", i)], d$f0medzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0median",
section = "entireExp",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 4 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One converged (impersonal condition) and three diverged (2 in impersonal and 1 in close condition.)
print(count)## speaker type direction coefficient mock real condition
## 1 FWR-A synchrony synchrony -0.300563638303997 0 no close
## 2 FWR-B synchrony synchrony -0.0903338172619992 0 no close
## 3 HAG-A synchrony dyssynchrony 0.09824684292749 0 no close
## 4 HAG-B synchrony synchrony -0.0638284953170705 0 no close
## 5 HBR-A synchrony dyssynchrony 0.0299827656475949 0 no impersonal
## 6 HBR-B synchrony dyssynchrony 0.116881271324112 0 no impersonal
## 7 HUJ-A synchrony dyssynchrony 0.201242839663773 0 yes close
## 8 HUJ-B synchrony dyssynchrony 0.0627937017123163 0 no close
## 9 KDA-A synchrony dyssynchrony 0.121636944685442 0 no impersonal
## 10 KDA-B synchrony dyssynchrony 0.0538269394660566 0 no impersonal
## 11 KPB-B synchrony dyssynchrony 0.127578136161196 0 no close
## 12 MJG-A synchrony dyssynchrony 0.0899603891734553 0 no close
## 13 NLO-A synchrony dyssynchrony 0.147066488514547 0 no close
## 14 OAL-A synchrony dyssynchrony 0.0959107135753522 0 no impersonal
## 15 OAL-B synchrony dyssynchrony 0.143153142681874 0 no impersonal
## 16 OXQ-A synchrony dyssynchrony 0.00271900872204129 0 no impersonal
## 17 OXQ-B synchrony dyssynchrony 0.0498139093450386 0 no impersonal
## 18 QRT-A synchrony synchrony -0.11427055616047 0 no close
## 19 QRT-B synchrony synchrony -0.0553655168261072 0 no close
## 20 SGB-B synchrony synchrony -0.0729455287064786 0 no impersonal
## 21 SUK-A synchrony dyssynchrony 0.159852903688834 0 yes impersonal
## 22 SUK-B synchrony dyssynchrony 0.150288323630953 0 no impersonal
## 23 TTN-A synchrony dyssynchrony 0.0346756305164721 0 no close
## 24 TTN-B synchrony synchrony -0.0801561638823987 0 no close
## 25 TTY-A synchrony synchrony -0.11718419455687 0 no close
## 26 TTY-B synchrony synchrony -0.0694356817891461 0 no close
## 27 VDE-A synchrony synchrony -0.00698703654527092 0 no impersonal
## 28 ZNV-A synchrony dyssynchrony 0.161701332123916 0 no close
## 29 ZNV-B synchrony synchrony -0.0457097777453476 0 no close
## 30 AML-A synchrony dyssynchrony 0.0903646112279503 1 no impersonal
## 31 AML-B synchrony synchrony -0.146257952336042 1 no impersonal
## 32 KPB-A synchrony dyssynchrony 0.0313304212491666 1 no close
## 33 MJG-B synchrony dyssynchrony 0.0207224162094005 1 no close
## 34 NLO-B synchrony dyssynchrony 0.0461495226329855 1 no close
## 35 SGB-A synchrony synchrony -0.00858759410310959 1 no impersonal
## 36 VDE-B synchrony dyssynchrony 0.104140507293413 2 no impersonal
## 37 FXO-A synchrony dyssynchrony 0.214568139759 4 no impersonal
## 38 FXO-B synchrony dyssynchrony 0.258429839383353 5 no impersonal
## feature section
## 1 f0median entireExp
## 2 f0median entireExp
## 3 f0median entireExp
## 4 f0median entireExp
## 5 f0median entireExp
## 6 f0median entireExp
## 7 f0median entireExp
## 8 f0median entireExp
## 9 f0median entireExp
## 10 f0median entireExp
## 11 f0median entireExp
## 12 f0median entireExp
## 13 f0median entireExp
## 14 f0median entireExp
## 15 f0median entireExp
## 16 f0median entireExp
## 17 f0median entireExp
## 18 f0median entireExp
## 19 f0median entireExp
## 20 f0median entireExp
## 21 f0median entireExp
## 22 f0median entireExp
## 23 f0median entireExp
## 24 f0median entireExp
## 25 f0median entireExp
## 26 f0median entireExp
## 27 f0median entireExp
## 28 f0median entireExp
## 29 f0median entireExp
## 30 f0median entireExp
## 31 f0median entireExp
## 32 f0median entireExp
## 33 f0median entireExp
## 34 f0median entireExp
## 35 f0median entireExp
## 36 f0median entireExp
## 37 f0median entireExp
## 38 f0median entireExpOnly in the first part of the experiment
ggplot(dat |> filter(task=="Lists"), aes(f0medzGender, prevf0med))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 133 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 133 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Lists")
c <- cor.test(d$f0medzGender, d$prevf0med,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMed", i)], d$f0medzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0median",
section = "Lists",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 4 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One in the close condition synchronized and two in the close condition desynchronized. One in the impersonal condition desynchronized.
print(count)## speaker type direction coefficient mock real condition
## 1 FWR-A synchrony synchrony -0.300563638303997 0 no close
## 2 FWR-B synchrony synchrony -0.0903338172619992 0 no close
## 3 FXO-A synchrony synchrony -0.063259892754004 0 no impersonal
## 4 FXO-B synchrony dyssynchrony 0.0779481438661408 0 no impersonal
## 5 HAG-B synchrony synchrony -0.04935367620688 0 no close
## 6 HUJ-A synchrony dyssynchrony 0.357298884252694 0 yes close
## 7 KDA-A synchrony dyssynchrony 0.29803616352212 0 no impersonal
## 8 KPB-A synchrony synchrony -0.242487684841087 0 no close
## 9 KPB-B synchrony dyssynchrony 0.0403473420771306 0 no close
## 10 MJG-A synchrony dyssynchrony 0.326241765898523 0 no close
## 11 MJG-B synchrony dyssynchrony 0.175598035954763 0 no close
## 12 OAL-A synchrony dyssynchrony 0.0144211222005726 0 no impersonal
## 13 OAL-B synchrony dyssynchrony 0.105738922640311 0 no impersonal
## 14 OXQ-A synchrony synchrony -0.0606462290828247 0 no impersonal
## 15 OXQ-B synchrony dyssynchrony 0.227580037512609 0 no impersonal
## 16 SUK-B synchrony synchrony -0.0105685543978161 0 no impersonal
## 17 TTN-A synchrony dyssynchrony 0.0560701443425562 0 no close
## 18 TTY-A synchrony synchrony -0.0800403479714815 0 no close
## 19 TTY-B synchrony synchrony -0.240791450468928 0 no close
## 20 VDE-B synchrony dyssynchrony 0.16117926018401 0 no impersonal
## 21 ZNV-A synchrony synchrony -0.246215269837491 0 no close
## 22 AML-A synchrony dyssynchrony 0.205251261385981 1 no impersonal
## 23 AML-B synchrony synchrony -0.180673728890839 1 no impersonal
## 24 HBR-A synchrony dyssynchrony 0.17579959866002 1 no impersonal
## 25 HBR-B synchrony dyssynchrony 0.167399575324429 1 no impersonal
## 26 HUJ-B synchrony synchrony -0.0311284685362079 1 no close
## 27 KDA-B synchrony synchrony -0.0300682517309385 1 no impersonal
## 28 NLO-B synchrony synchrony -0.0488085453207347 1 no close
## 29 QRT-A synchrony synchrony -0.390678503584988 1 yes close
## 30 QRT-B synchrony synchrony -0.154361931658257 1 no close
## 31 SGB-B synchrony synchrony -0.132313848153157 1 no impersonal
## 32 TTN-B synchrony synchrony -0.245953026473935 1 no close
## 33 VDE-A synchrony dyssynchrony 0.163771790713542 1 no impersonal
## 34 ZNV-B synchrony dyssynchrony 0.078874667911933 1 no close
## 35 HAG-A synchrony dyssynchrony 0.0842630443752847 2 no close
## 36 NLO-A synchrony dyssynchrony 0.427258755786233 2 no close
## 37 SGB-A synchrony synchrony -0.329082926590209 2 no impersonal
## 38 SUK-A synchrony dyssynchrony 0.0369339511639239 2 no impersonal
## feature section
## 1 f0median Lists
## 2 f0median Lists
## 3 f0median Lists
## 4 f0median Lists
## 5 f0median Lists
## 6 f0median Lists
## 7 f0median Lists
## 8 f0median Lists
## 9 f0median Lists
## 10 f0median Lists
## 11 f0median Lists
## 12 f0median Lists
## 13 f0median Lists
## 14 f0median Lists
## 15 f0median Lists
## 16 f0median Lists
## 17 f0median Lists
## 18 f0median Lists
## 19 f0median Lists
## 20 f0median Lists
## 21 f0median Lists
## 22 f0median Lists
## 23 f0median Lists
## 24 f0median Lists
## 25 f0median Lists
## 26 f0median Lists
## 27 f0median Lists
## 28 f0median Lists
## 29 f0median Lists
## 30 f0median Lists
## 31 f0median Lists
## 32 f0median Lists
## 33 f0median Lists
## 34 f0median Lists
## 35 f0median Lists
## 36 f0median Lists
## 37 f0median Lists
## 38 f0median ListsOnly in the second part of the experiment
ggplot(dat |> filter(task=="Diapix"), aes(f0medzGender, prevf0med))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 297 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 297 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Diapix")
if(nrow(d) ==0){next} # because we don't have the Diapix files for one dyad
c <- cor.test(d$f0medzGender, d$prevf0med,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMed", i)], d$f0medzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0median",
section = "Diapix",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 3 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
All three desynchronized. One in the close condition and two in the impersonal condition.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-B synchrony synchrony -0.111397648658917 0 no impersonal
## 2 FXO-B synchrony dyssynchrony 0.155415765102268 0 no impersonal
## 3 HAG-A synchrony dyssynchrony 0.136821613219354 0 no close
## 4 HAG-B synchrony synchrony -0.0857910037843134 0 no close
## 5 HBR-A synchrony synchrony -0.053747580897934 0 no impersonal
## 6 HUJ-A synchrony dyssynchrony 0.148127454501519 0 no close
## 7 HUJ-B synchrony dyssynchrony 0.127850385010129 0 no close
## 8 KDA-A synchrony dyssynchrony 0.00845859250152412 0 no impersonal
## 9 KDA-B synchrony synchrony -0.0657278051300379 0 no impersonal
## 10 KPB-B synchrony dyssynchrony 0.139222654174925 0 no close
## 11 MJG-A synchrony dyssynchrony 0.0805659334777255 0 no close
## 12 NLO-A synchrony dyssynchrony 0.125411269271292 0 no close
## 13 NLO-B synchrony dyssynchrony 0.0512109354640375 0 no close
## 14 OAL-B synchrony dyssynchrony 0.109543374380144 0 no impersonal
## 15 OXQ-A synchrony dyssynchrony 0.0897795595989093 0 no impersonal
## 16 OXQ-B synchrony synchrony -0.00720796902206883 0 no impersonal
## 17 QRT-B synchrony synchrony -0.00563172603242151 0 no close
## 18 SUK-A synchrony dyssynchrony 0.183856357146092 0 yes impersonal
## 19 SUK-B synchrony dyssynchrony 0.21883773693464 0 yes impersonal
## 20 TTN-A synchrony dyssynchrony 0.0200452345903258 0 no close
## 21 TTY-A synchrony synchrony -0.0671341631951863 0 no close
## 22 TTY-B synchrony dyssynchrony 0.0247270558802934 0 no close
## 23 VDE-A synchrony synchrony -0.0386510029682614 0 no impersonal
## 24 ZNV-A synchrony dyssynchrony 0.342883979389005 0 yes close
## 25 AML-A synchrony synchrony -0.0503773929497733 1 no impersonal
## 26 FXO-A synchrony dyssynchrony 0.179843226600184 1 no impersonal
## 27 HBR-B synchrony dyssynchrony 0.132352368457503 1 no impersonal
## 28 KPB-A synchrony dyssynchrony 0.088974228076865 1 no close
## 29 MJG-B synchrony synchrony -0.00998674300858751 1 no close
## 30 QRT-A synchrony synchrony -0.00214849953654202 1 no close
## 31 SGB-A synchrony dyssynchrony 0.0626848133635434 1 no impersonal
## 32 SGB-B synchrony synchrony -0.0119655000463638 1 no impersonal
## 33 TTN-B synchrony synchrony -0.0171964234067967 1 no close
## 34 ZNV-B synchrony synchrony -0.115007287539743 1 no close
## 35 VDE-B synchrony dyssynchrony 0.103111017933215 2 no impersonal
## 36 OAL-A synchrony dyssynchrony 0.116673568921843 4 no impersonal
## feature section
## 1 f0median Diapix
## 2 f0median Diapix
## 3 f0median Diapix
## 4 f0median Diapix
## 5 f0median Diapix
## 6 f0median Diapix
## 7 f0median Diapix
## 8 f0median Diapix
## 9 f0median Diapix
## 10 f0median Diapix
## 11 f0median Diapix
## 12 f0median Diapix
## 13 f0median Diapix
## 14 f0median Diapix
## 15 f0median Diapix
## 16 f0median Diapix
## 17 f0median Diapix
## 18 f0median Diapix
## 19 f0median Diapix
## 20 f0median Diapix
## 21 f0median Diapix
## 22 f0median Diapix
## 23 f0median Diapix
## 24 f0median Diapix
## 25 f0median Diapix
## 26 f0median Diapix
## 27 f0median Diapix
## 28 f0median Diapix
## 29 f0median Diapix
## 30 f0median Diapix
## 31 f0median Diapix
## 32 f0median Diapix
## 33 f0median Diapix
## 34 f0median Diapix
## 35 f0median Diapix
## 36 f0median Diapixf0 max
Across the entire experiment
ggplot(dat, aes(f0maxzGender, prevf0max))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 288 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 288 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s)
c <- cor.test(d$f0maxzGender, d$prevf0max,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMax", i)], d$f0maxzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0max",
section = "entireExp",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 8 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
All desynchronized. Two in the close condition and six in the impersonal condition.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A synchrony dyssynchrony 0.0416290849805265 0 no impersonal
## 2 HBR-A synchrony dyssynchrony 0.0876169357960696 0 no impersonal
## 3 HUJ-A synchrony dyssynchrony 0.034557190796969 0 no close
## 4 KPB-A synchrony dyssynchrony 0.168192467942173 0 no close
## 5 KPB-B synchrony synchrony -0.011328666437045 0 no close
## 6 NLO-A synchrony dyssynchrony 0.255974952041841 0 yes close
## 7 OAL-A synchrony dyssynchrony 0.221544511147051 0 yes impersonal
## 8 OXQ-A synchrony synchrony -0.0131916423013826 0 no impersonal
## 9 OXQ-B synchrony dyssynchrony 0.175236749050539 0 yes impersonal
## 10 QRT-A synchrony dyssynchrony 0.120857015665861 0 no close
## 11 QRT-B synchrony synchrony -0.024401104268546 0 no close
## 12 SGB-A synchrony dyssynchrony 0.0840549317421198 0 no impersonal
## 13 SGB-B synchrony synchrony -0.0186687176452995 0 no impersonal
## 14 SUK-B synchrony dyssynchrony 0.130713222242466 0 no impersonal
## 15 TTY-A synchrony dyssynchrony 0.202849083353503 0 yes close
## 16 VDE-A synchrony dyssynchrony 0.126090128945892 0 no impersonal
## 17 ZNV-B synchrony synchrony -0.0212148801392571 0 no close
## 18 AML-B synchrony dyssynchrony 0.25550299231443 1 yes impersonal
## 19 FWR-A synchrony dyssynchrony 0.00488048850626231 1 no close
## 20 FWR-B synchrony synchrony -0.458183982035592 1 no close
## 21 HAG-B synchrony dyssynchrony 0.0119834422507738 1 no close
## 22 HBR-B synchrony dyssynchrony 0.0243226567285494 1 no impersonal
## 23 KDA-A synchrony dyssynchrony 0.0238123332891506 1 no impersonal
## 24 MJG-A synchrony dyssynchrony 0.03242382587976 1 no close
## 25 OAL-B synchrony dyssynchrony 0.0802037875378496 1 no impersonal
## 26 TTN-A synchrony dyssynchrony 0.182537194609077 1 yes close
## 27 TTN-B synchrony dyssynchrony 0.113839227852142 1 no close
## 28 VDE-B synchrony dyssynchrony 0.182133613328821 1 yes impersonal
## 29 ZNV-A synchrony dyssynchrony 0.0415904824426567 1 no close
## 30 FXO-A synchrony dyssynchrony 0.126035724283629 2 no impersonal
## 31 FXO-B synchrony dyssynchrony 0.212665765727217 2 no impersonal
## 32 HUJ-B synchrony dyssynchrony 0.0158766721929422 2 no close
## 33 KDA-B synchrony dyssynchrony 0.0801031840038905 2 no impersonal
## 34 MJG-B synchrony synchrony -0.0536695411162715 2 no close
## 35 NLO-B synchrony dyssynchrony 0.145453514431542 2 no close
## 36 SUK-A synchrony dyssynchrony 0.101159077096394 2 no impersonal
## 37 TTY-B synchrony dyssynchrony 0.108886755543673 2 no close
## 38 HAG-A synchrony synchrony -0.0652759110843368 3 no close
## feature section
## 1 f0max entireExp
## 2 f0max entireExp
## 3 f0max entireExp
## 4 f0max entireExp
## 5 f0max entireExp
## 6 f0max entireExp
## 7 f0max entireExp
## 8 f0max entireExp
## 9 f0max entireExp
## 10 f0max entireExp
## 11 f0max entireExp
## 12 f0max entireExp
## 13 f0max entireExp
## 14 f0max entireExp
## 15 f0max entireExp
## 16 f0max entireExp
## 17 f0max entireExp
## 18 f0max entireExp
## 19 f0max entireExp
## 20 f0max entireExp
## 21 f0max entireExp
## 22 f0max entireExp
## 23 f0max entireExp
## 24 f0max entireExp
## 25 f0max entireExp
## 26 f0max entireExp
## 27 f0max entireExp
## 28 f0max entireExp
## 29 f0max entireExp
## 30 f0max entireExp
## 31 f0max entireExp
## 32 f0max entireExp
## 33 f0max entireExp
## 34 f0max entireExp
## 35 f0max entireExp
## 36 f0max entireExp
## 37 f0max entireExp
## 38 f0max entireExpOnly in the first part of the experiment
ggplot(dat |> filter(task=="Lists"), aes(f0maxzGender, prevf0max))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 97 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 97 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Lists")
c <- cor.test(d$f0maxzGender, d$prevf0max,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMax", i)], d$f0maxzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0max",
section = "Lists",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the only speaker whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
She was in the impersonal condition and desynchronized.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A synchrony dyssynchrony 0.141164436287923 0 no impersonal
## 2 AML-B synchrony dyssynchrony 0.286064558589535 0 no impersonal
## 3 FXO-B synchrony dyssynchrony 0.142735223250187 0 no impersonal
## 4 HBR-A synchrony dyssynchrony 0.0395786727064874 0 no impersonal
## 5 HBR-B synchrony dyssynchrony 0.0809436405355996 0 no impersonal
## 6 KPB-A synchrony dyssynchrony 0.221428089361738 0 no close
## 7 KPB-B synchrony synchrony -0.125722531809469 0 no close
## 8 MJG-A synchrony synchrony -0.0892656125884824 0 no close
## 9 NLO-A synchrony synchrony -0.12147251220541 0 no close
## 10 OAL-A synchrony dyssynchrony 0.263851732546551 0 no impersonal
## 11 OAL-B synchrony dyssynchrony 0.044274566049483 0 no impersonal
## 12 OXQ-B synchrony synchrony -0.0921078693092518 0 no impersonal
## 13 QRT-A synchrony dyssynchrony 0.257713557270937 0 no close
## 14 SUK-B synchrony dyssynchrony 0.200574888337046 0 no impersonal
## 15 TTN-B synchrony dyssynchrony 0.0557646248209323 0 no close
## 16 VDE-A synchrony dyssynchrony 0.33594454744154 0 yes impersonal
## 17 VDE-B synchrony dyssynchrony 0.231721334652014 0 no impersonal
## 18 ZNV-A synchrony dyssynchrony 0.0238191058055579 0 no close
## 19 ZNV-B synchrony synchrony -0.299219279409205 0 no close
## 20 FWR-A synchrony dyssynchrony 0.00488048850626231 1 no close
## 21 FWR-B synchrony synchrony -0.458183982035592 1 no close
## 22 FXO-A synchrony synchrony -0.0438226314347165 1 no impersonal
## 23 HAG-A synchrony synchrony -0.164418703093826 1 no close
## 24 HAG-B synchrony synchrony -0.250571843644055 1 no close
## 25 HUJ-A synchrony dyssynchrony 0.0924757706836303 1 no close
## 26 OXQ-A synchrony synchrony -0.0983050587633334 1 no impersonal
## 27 QRT-B synchrony dyssynchrony 0.217659700033205 1 no close
## 28 SGB-A synchrony synchrony -0.0795490567607825 1 no impersonal
## 29 SGB-B synchrony dyssynchrony 0.142681325407843 1 no impersonal
## 30 SUK-A synchrony synchrony -0.0450230251337383 1 no impersonal
## 31 TTN-A synchrony dyssynchrony 0.0808854876316062 1 no close
## 32 MJG-B synchrony synchrony -0.00603329694532394 2 no close
## 33 NLO-B synchrony dyssynchrony 0.00975205123211711 2 no close
## 34 TTY-A synchrony dyssynchrony 0.35970500423317 2 no close
## 35 TTY-B synchrony synchrony -0.159978937047311 2 no close
## 36 HUJ-B synchrony synchrony -0.200099113333065 3 no close
## 37 KDA-B synchrony dyssynchrony 0.547561911098476 6 no impersonal
## 38 KDA-A synchrony dyssynchrony 0.506831733385603 7 no impersonal
## feature section
## 1 f0max Lists
## 2 f0max Lists
## 3 f0max Lists
## 4 f0max Lists
## 5 f0max Lists
## 6 f0max Lists
## 7 f0max Lists
## 8 f0max Lists
## 9 f0max Lists
## 10 f0max Lists
## 11 f0max Lists
## 12 f0max Lists
## 13 f0max Lists
## 14 f0max Lists
## 15 f0max Lists
## 16 f0max Lists
## 17 f0max Lists
## 18 f0max Lists
## 19 f0max Lists
## 20 f0max Lists
## 21 f0max Lists
## 22 f0max Lists
## 23 f0max Lists
## 24 f0max Lists
## 25 f0max Lists
## 26 f0max Lists
## 27 f0max Lists
## 28 f0max Lists
## 29 f0max Lists
## 30 f0max Lists
## 31 f0max Lists
## 32 f0max Lists
## 33 f0max Lists
## 34 f0max Lists
## 35 f0max Lists
## 36 f0max Lists
## 37 f0max Lists
## 38 f0max ListsOnly in the second part of the experiment
ggplot(dat |> filter(task=="Diapix"), aes(f0maxzGender, prevf0max))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 191 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 191 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Diapix")
if(nrow(d) ==0){next} # because we don't have the Diapix files for one dyad
c <- cor.test(d$f0maxzGender, d$prevf0max,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockMax", i)], d$f0maxzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0max",
section = "Diapix",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 3 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
All desynchronized, one in the close and two in the impersonal condition.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-B synchrony dyssynchrony 0.231186577537179 0 yes impersonal
## 2 FXO-A synchrony dyssynchrony 0.0309467662641052 0 no impersonal
## 3 HAG-B synchrony dyssynchrony 0.0424697881468647 0 no close
## 4 HBR-A synchrony dyssynchrony 0.102811584240309 0 no impersonal
## 5 HUJ-A synchrony dyssynchrony 0.00336018202987396 0 no close
## 6 KDA-B synchrony synchrony -0.0349554159592185 0 no impersonal
## 7 KPB-A synchrony dyssynchrony 0.201144438843265 0 no close
## 8 KPB-B synchrony dyssynchrony 0.00835090643953848 0 no close
## 9 MJG-B synchrony synchrony -0.0680501305177131 0 no close
## 10 NLO-A synchrony dyssynchrony 0.338057672770634 0 yes close
## 11 OAL-A synchrony dyssynchrony 0.123090608957763 0 no impersonal
## 12 OXQ-A synchrony dyssynchrony 0.0345958594057422 0 no impersonal
## 13 OXQ-B synchrony dyssynchrony 0.30774262109949 0 yes impersonal
## 14 QRT-A synchrony dyssynchrony 0.0452485126722081 0 no close
## 15 QRT-B synchrony synchrony -0.117308849293399 0 no close
## 16 SGB-A synchrony dyssynchrony 0.0845694521802394 0 no impersonal
## 17 SGB-B synchrony synchrony -0.0525138912761415 0 no impersonal
## 18 SUK-B synchrony dyssynchrony 0.105829005808465 0 no impersonal
## 19 TTN-A synchrony dyssynchrony 0.237696697413486 0 yes close
## 20 TTY-A synchrony dyssynchrony 0.127492156571264 0 no close
## 21 VDE-A synchrony dyssynchrony 0.0996299439856212 0 no impersonal
## 22 VDE-B synchrony dyssynchrony 0.162886734111321 0 no impersonal
## 23 ZNV-B synchrony dyssynchrony 0.114597517731536 0 no close
## 24 AML-A synchrony synchrony -0.00216358854450929 1 no impersonal
## 25 FXO-B synchrony synchrony -0.0145082806110858 1 no impersonal
## 26 HAG-A synchrony synchrony -0.110860198592555 1 no close
## 27 HBR-B synchrony synchrony -0.016550700900653 1 no impersonal
## 28 HUJ-B synchrony dyssynchrony 0.159576793779983 1 no close
## 29 KDA-A synchrony synchrony -0.12543875849989 1 no impersonal
## 30 MJG-A synchrony dyssynchrony 0.0436207296924935 1 no close
## 31 OAL-B synchrony dyssynchrony 0.0534729193217042 1 no impersonal
## 32 TTN-B synchrony dyssynchrony 0.105651994592981 1 no close
## 33 TTY-B synchrony dyssynchrony 0.185729023598761 1 no close
## 34 ZNV-A synchrony dyssynchrony 0.122262081306364 1 no close
## 35 NLO-B synchrony dyssynchrony 0.167318687271417 2 no close
## 36 SUK-A synchrony dyssynchrony 0.109444786824869 2 no impersonal
## feature section
## 1 f0max Diapix
## 2 f0max Diapix
## 3 f0max Diapix
## 4 f0max Diapix
## 5 f0max Diapix
## 6 f0max Diapix
## 7 f0max Diapix
## 8 f0max Diapix
## 9 f0max Diapix
## 10 f0max Diapix
## 11 f0max Diapix
## 12 f0max Diapix
## 13 f0max Diapix
## 14 f0max Diapix
## 15 f0max Diapix
## 16 f0max Diapix
## 17 f0max Diapix
## 18 f0max Diapix
## 19 f0max Diapix
## 20 f0max Diapix
## 21 f0max Diapix
## 22 f0max Diapix
## 23 f0max Diapix
## 24 f0max Diapix
## 25 f0max Diapix
## 26 f0max Diapix
## 27 f0max Diapix
## 28 f0max Diapix
## 29 f0max Diapix
## 30 f0max Diapix
## 31 f0max Diapix
## 32 f0max Diapix
## 33 f0max Diapix
## 34 f0max Diapix
## 35 f0max Diapix
## 36 f0max Diapixf0 SD
Across the entire experiment
ggplot(dat, aes(f0sdzGender, prevf0sd))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 575 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 575 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s)
c <- cor.test(d$f0sdzGender, d$prevf0sd,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockSd", i)], d$f0sdzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0sd",
section = "entireExp",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 5 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
One synchronized (close condition). Four desynchronized (two close and two impersonal condition).
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A synchrony dyssynchrony 0.153778116855479 0 no impersonal
## 2 FWR-B synchrony synchrony -0.0324447261389338 0 no close
## 3 HAG-A synchrony synchrony -0.143766052888694 0 no close
## 4 HAG-B synchrony dyssynchrony 0.101313108390594 0 no close
## 5 KPB-B synchrony dyssynchrony 0.0541386350246041 0 no close
## 6 MJG-B synchrony dyssynchrony 0.0669785567362788 0 no close
## 7 NLO-A synchrony dyssynchrony 0.233737973133155 0 yes close
## 8 OAL-A synchrony dyssynchrony 0.140931898052414 0 no impersonal
## 9 OXQ-A synchrony synchrony -0.064906034559461 0 no impersonal
## 10 OXQ-B synchrony dyssynchrony 0.0927476551496673 0 no impersonal
## 11 QRT-A synchrony dyssynchrony 0.0513261627179744 0 no close
## 12 QRT-B synchrony synchrony -0.0766753934850746 0 no close
## 13 SGB-A synchrony dyssynchrony 0.0973015422340224 0 no impersonal
## 14 SGB-B synchrony dyssynchrony 0.0459369140109435 0 no impersonal
## 15 SUK-B synchrony dyssynchrony 0.065794034576589 0 no impersonal
## 16 TTN-A synchrony dyssynchrony 0.237874092353268 0 yes close
## 17 TTY-A synchrony dyssynchrony 0.124730222992609 0 no close
## 18 ZNV-A synchrony synchrony -0.061237698977345 0 no close
## 19 AML-B synchrony dyssynchrony 0.201950622475474 1 yes impersonal
## 20 FXO-A synchrony dyssynchrony 0.15031168921442 1 no impersonal
## 21 FXO-B synchrony dyssynchrony 0.156992144420927 1 no impersonal
## 22 HBR-A synchrony dyssynchrony 0.128832131269147 1 no impersonal
## 23 HUJ-A synchrony dyssynchrony 0.0638984110581506 1 no close
## 24 HUJ-B synchrony dyssynchrony 0.0469235220894952 1 no close
## 25 KDA-A synchrony synchrony -0.0178123859239933 1 no impersonal
## 26 KDA-B synchrony synchrony -0.115054447034382 1 no impersonal
## 27 KPB-A synchrony dyssynchrony 0.0300078944661093 1 no close
## 28 NLO-B synchrony synchrony -0.0244629622292923 1 no close
## 29 OAL-B synchrony dyssynchrony 0.141088237854238 1 no impersonal
## 30 SUK-A synchrony dyssynchrony 0.127197201703565 1 no impersonal
## 31 TTN-B synchrony dyssynchrony 0.0524129574686659 1 no close
## 32 TTY-B synchrony synchrony -0.145003218983133 1 no close
## 33 VDE-A synchrony dyssynchrony 0.23120492741628 1 yes impersonal
## 34 ZNV-B synchrony dyssynchrony 0.0683674782309375 1 no close
## 35 FWR-A synchrony dyssynchrony 0.0675572805781528 2 no close
## 36 HBR-B synchrony dyssynchrony 0.0934297243675509 2 no impersonal
## 37 MJG-A synchrony dyssynchrony 0.0251522232559881 2 no close
## 38 VDE-B synchrony dyssynchrony 0.152908863291665 4 no impersonal
## feature section
## 1 f0sd entireExp
## 2 f0sd entireExp
## 3 f0sd entireExp
## 4 f0sd entireExp
## 5 f0sd entireExp
## 6 f0sd entireExp
## 7 f0sd entireExp
## 8 f0sd entireExp
## 9 f0sd entireExp
## 10 f0sd entireExp
## 11 f0sd entireExp
## 12 f0sd entireExp
## 13 f0sd entireExp
## 14 f0sd entireExp
## 15 f0sd entireExp
## 16 f0sd entireExp
## 17 f0sd entireExp
## 18 f0sd entireExp
## 19 f0sd entireExp
## 20 f0sd entireExp
## 21 f0sd entireExp
## 22 f0sd entireExp
## 23 f0sd entireExp
## 24 f0sd entireExp
## 25 f0sd entireExp
## 26 f0sd entireExp
## 27 f0sd entireExp
## 28 f0sd entireExp
## 29 f0sd entireExp
## 30 f0sd entireExp
## 31 f0sd entireExp
## 32 f0sd entireExp
## 33 f0sd entireExp
## 34 f0sd entireExp
## 35 f0sd entireExp
## 36 f0sd entireExp
## 37 f0sd entireExp
## 38 f0sd entireExpOnly in the first part of the experiment
ggplot(dat |> filter(task=="Lists"), aes(f0sdzGender, prevf0sd))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 152 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 152 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Lists")
c <- cor.test(d$f0sdzGender, d$prevf0sd,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockSd", i)], d$f0sdzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0sd",
section = "Lists",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 2 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
Both desynchronized and were in the close condition.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A synchrony dyssynchrony 0.0611655649472113 0 no impersonal
## 2 AML-B synchrony dyssynchrony 0.30879429857356 0 yes impersonal
## 3 FWR-B synchrony synchrony -0.0324447261389338 0 no close
## 4 FXO-B synchrony dyssynchrony 0.144170396842999 0 no impersonal
## 5 HAG-B synchrony synchrony -0.0881146231300642 0 no close
## 6 HUJ-A synchrony synchrony -0.120845396418935 0 no close
## 7 HUJ-B synchrony synchrony -0.00122613312587873 0 no close
## 8 KPB-A synchrony dyssynchrony 0.216017865393854 0 no close
## 9 KPB-B synchrony dyssynchrony 0.00547995821039961 0 no close
## 10 MJG-A synchrony synchrony -0.199425800143578 0 no close
## 11 NLO-B synchrony dyssynchrony 0.348000337617956 0 no close
## 12 OXQ-B synchrony dyssynchrony 0.00823109981076577 0 no impersonal
## 13 SGB-B synchrony dyssynchrony 0.00640269094968847 0 no impersonal
## 14 SUK-A synchrony synchrony -0.200323911932563 0 no impersonal
## 15 SUK-B synchrony dyssynchrony 0.25552836130833 0 no impersonal
## 16 TTN-A synchrony dyssynchrony 0.135266323710738 0 no close
## 17 TTY-A synchrony dyssynchrony 0.424723946652559 0 yes close
## 18 TTY-B synchrony synchrony -0.0831448789691296 0 no close
## 19 FXO-A synchrony synchrony -0.075791288630783 1 no impersonal
## 20 HAG-A synchrony synchrony -0.404067610268608 1 yes close
## 21 HBR-A synchrony dyssynchrony 0.00766945422939426 1 no impersonal
## 22 HBR-B synchrony synchrony -0.222562021258751 1 no impersonal
## 23 MJG-B synchrony dyssynchrony 0.241740773762611 1 no close
## 24 NLO-A synchrony synchrony -0.106304536899728 1 no close
## 25 OAL-A synchrony dyssynchrony 0.0295696473878664 1 no impersonal
## 26 OAL-B synchrony synchrony -0.000262610315730354 1 no impersonal
## 27 QRT-A synchrony synchrony -0.164210783239977 1 no close
## 28 QRT-B synchrony dyssynchrony 0.253569805339333 1 no close
## 29 SGB-A synchrony synchrony -0.0278966219632956 1 no impersonal
## 30 TTN-B synchrony dyssynchrony 0.187019438332667 1 no close
## 31 VDE-B synchrony dyssynchrony 0.0728138821854779 1 no impersonal
## 32 ZNV-A synchrony dyssynchrony 0.09751919262334 1 no close
## 33 ZNV-B synchrony synchrony -0.254309421919819 1 no close
## 34 FWR-A synchrony dyssynchrony 0.0675572805781528 2 no close
## 35 KDA-A synchrony dyssynchrony 0.46078306524803 2 no impersonal
## 36 KDA-B synchrony dyssynchrony 0.323983027691163 2 no impersonal
## 37 OXQ-A synchrony synchrony -0.163513678774864 2 no impersonal
## 38 VDE-A synchrony dyssynchrony 0.103449969897939 2 no impersonal
## feature section
## 1 f0sd Lists
## 2 f0sd Lists
## 3 f0sd Lists
## 4 f0sd Lists
## 5 f0sd Lists
## 6 f0sd Lists
## 7 f0sd Lists
## 8 f0sd Lists
## 9 f0sd Lists
## 10 f0sd Lists
## 11 f0sd Lists
## 12 f0sd Lists
## 13 f0sd Lists
## 14 f0sd Lists
## 15 f0sd Lists
## 16 f0sd Lists
## 17 f0sd Lists
## 18 f0sd Lists
## 19 f0sd Lists
## 20 f0sd Lists
## 21 f0sd Lists
## 22 f0sd Lists
## 23 f0sd Lists
## 24 f0sd Lists
## 25 f0sd Lists
## 26 f0sd Lists
## 27 f0sd Lists
## 28 f0sd Lists
## 29 f0sd Lists
## 30 f0sd Lists
## 31 f0sd Lists
## 32 f0sd Lists
## 33 f0sd Lists
## 34 f0sd Lists
## 35 f0sd Lists
## 36 f0sd Lists
## 37 f0sd Lists
## 38 f0sd ListsOnly in the second part of the experiment
ggplot(dat |> filter(task=="Diapix"), aes(f0sdzGender, prevf0sd))+
geom_point()+
geom_smooth(method="lm")+
facet_wrap(~speaker)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 423 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 423 rows containing missing values (`geom_point()`).
corS <- data.frame(matrix(nrow=0, ncol=4))
names(corS) <- c("speaker", "type", "r", "p")
for(s in unique(dat$speaker)){
d <- dat |> filter(speaker==s, task=="Diapix")
if(nrow(d) ==0){next} # because we don't have the Diapix files for one dyad
c <- cor.test(d$f0sdzGender, d$prevf0sd,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "real", c$estimate, c$p.value)
for(i in 1:10){
c <- cor.test(d[,paste0("mockSd", i)], d$f0sdzGender,
alternative="two.sided", method="pearson")
corS[nrow(corS)+1,] <- c(s, "mock", c$estimate, c$p.value)
}
}
corS <- corS |>
mutate(sign = ifelse(p < 0.05, "*", NA),
direction = case_when(
r < 0 ~ "synchrony",
r > 0 ~ "dyssynchrony"))
count <- data.frame(matrix(nrow=0, ncol=6))
names(count) <- c("speaker", "type", "direction", "coefficient", "mock", "real")
for(s in unique(corS$speaker)){
count[nrow(count)+1,] <- c(s,
"synchrony",
corS$direction[corS$speaker==s & corS$type=="real"],
corS$r[corS$speaker==s & corS$type=="real"],
sum(!is.na(corS$sign[corS$speaker==s & corS$type=="mock"])==TRUE),
ifelse(corS$sign[corS$speaker==s & corS$type=="real"]=="*", "yes", "no"))
}
count <- merge(count, dat |> select(condition, speaker) |> filter(!duplicated(speaker)), by ="speaker")
count <- count |>
mutate_at("mock", as.numeric) |>
arrange(mock) |>
mutate(feature = "f0sd",
section = "Diapix",
real = case_when(
mock > 1 | is.na(real) ~ "no",
.default = as.character(real)
))
all <- rbind(all, count)Below we see the 4 speakers whose correlation was significant between time and real f0 median differences AND who had no more than 1 significant correlation between time and randomly ordered f0 median.
All desynchronized – two in the close and two in the impersonal condition.
print(count)## speaker type direction coefficient mock real condition
## 1 AML-A synchrony dyssynchrony 0.213149040230424 0 no impersonal
## 2 FXO-A synchrony dyssynchrony 0.116267088295808 0 no impersonal
## 3 HAG-A synchrony synchrony -0.0258574451716526 0 no close
## 4 HUJ-B synchrony dyssynchrony 0.0694753771190249 0 no close
## 5 KDA-B synchrony synchrony -0.173814635133023 0 no impersonal
## 6 KPB-B synchrony dyssynchrony 0.0429610119228431 0 no close
## 7 NLO-A synchrony dyssynchrony 0.274685348789403 0 yes close
## 8 OXQ-B synchrony dyssynchrony 0.125944355427305 0 no impersonal
## 9 QRT-A synchrony dyssynchrony 0.0685951582052735 0 no close
## 10 QRT-B synchrony synchrony -0.103871471357015 0 no close
## 11 SUK-A synchrony dyssynchrony 0.122742433682649 0 no impersonal
## 12 TTN-A synchrony dyssynchrony 0.269606087931864 0 yes close
## 13 TTN-B synchrony dyssynchrony 0.0148518818339105 0 no close
## 14 VDE-A synchrony dyssynchrony 0.125461158248949 0 no impersonal
## 15 VDE-B synchrony dyssynchrony 0.0421230600989299 0 no impersonal
## 16 ZNV-A synchrony dyssynchrony 0.0164402610084308 0 no close
## 17 AML-B synchrony dyssynchrony 0.159665173881711 1 no impersonal
## 18 FXO-B synchrony dyssynchrony 0.0539337834963957 1 no impersonal
## 19 HAG-B synchrony dyssynchrony 0.144160714665811 1 no close
## 20 HBR-A synchrony dyssynchrony 0.18017574059799 1 no impersonal
## 21 HUJ-A synchrony dyssynchrony 0.0357576172231783 1 no close
## 22 KDA-A synchrony synchrony -0.04732298787283 1 no impersonal
## 23 KPB-A synchrony dyssynchrony 0.0419557813493141 1 no close
## 24 MJG-A synchrony dyssynchrony 0.0925991818141589 1 no close
## 25 MJG-B synchrony dyssynchrony 0.0395987436268099 1 no close
## 26 NLO-B synchrony synchrony -0.0566893964030592 1 no close
## 27 OAL-A synchrony dyssynchrony 0.0849493721184279 1 no impersonal
## 28 OAL-B synchrony dyssynchrony 0.1556678036417 1 no impersonal
## 29 OXQ-A synchrony synchrony -0.0262836588313215 1 no impersonal
## 30 SGB-A synchrony dyssynchrony 0.0936196772572417 1 no impersonal
## 31 SGB-B synchrony dyssynchrony 0.0518580350034459 1 no impersonal
## 32 SUK-B synchrony synchrony -0.0115946599908498 1 no impersonal
## 33 TTY-A synchrony dyssynchrony 0.0697079625476732 1 no close
## 34 HBR-B synchrony dyssynchrony 0.224525402973737 2 no impersonal
## 35 TTY-B synchrony synchrony -0.0835069709913098 2 no close
## 36 ZNV-B synchrony dyssynchrony 0.169712359137907 2 no close
## feature section
## 1 f0sd Diapix
## 2 f0sd Diapix
## 3 f0sd Diapix
## 4 f0sd Diapix
## 5 f0sd Diapix
## 6 f0sd Diapix
## 7 f0sd Diapix
## 8 f0sd Diapix
## 9 f0sd Diapix
## 10 f0sd Diapix
## 11 f0sd Diapix
## 12 f0sd Diapix
## 13 f0sd Diapix
## 14 f0sd Diapix
## 15 f0sd Diapix
## 16 f0sd Diapix
## 17 f0sd Diapix
## 18 f0sd Diapix
## 19 f0sd Diapix
## 20 f0sd Diapix
## 21 f0sd Diapix
## 22 f0sd Diapix
## 23 f0sd Diapix
## 24 f0sd Diapix
## 25 f0sd Diapix
## 26 f0sd Diapix
## 27 f0sd Diapix
## 28 f0sd Diapix
## 29 f0sd Diapix
## 30 f0sd Diapix
## 31 f0sd Diapix
## 32 f0sd Diapix
## 33 f0sd Diapix
## 34 f0sd Diapix
## 35 f0sd Diapix
## 36 f0sd DiapixAll effects
all <- all |>
mutate(sectionN = case_when(
section=="entireExp" ~ 1,
section=="Lists" ~ 2,
section=="Diapix" ~ 3
)) |>
rename(entrain=real) |>
arrange(sectionN) |>
select(-sectionN)
print(all |> filter(entrain=="yes"))## speaker type direction coefficient mock entrain condition
## 1 OXQ-B convergence divergence 0.166665816388928 0 yes impersonal
## 2 HBR-B convergence divergence 0.189733700626366 1 yes impersonal
## 3 OAL-A convergence convergence -0.298741868255242 0 yes impersonal
## 4 ZNV-B convergence convergence -0.201336693011536 0 yes close
## 5 HUJ-A synchrony dyssynchrony 0.201242839663773 0 yes close
## 6 SUK-A synchrony dyssynchrony 0.159852903688834 0 yes impersonal
## 7 NLO-A synchrony dyssynchrony 0.255974952041841 0 yes close
## 8 OAL-A synchrony dyssynchrony 0.221544511147051 0 yes impersonal
## 9 OXQ-B synchrony dyssynchrony 0.175236749050539 0 yes impersonal
## 10 TTY-A synchrony dyssynchrony 0.202849083353503 0 yes close
## 11 AML-B synchrony dyssynchrony 0.25550299231443 1 yes impersonal
## 12 TTN-A synchrony dyssynchrony 0.182537194609077 1 yes close
## 13 VDE-B synchrony dyssynchrony 0.182133613328821 1 yes impersonal
## 14 NLO-A synchrony dyssynchrony 0.233737973133155 0 yes close
## 15 TTN-A synchrony dyssynchrony 0.237874092353268 0 yes close
## 16 AML-B synchrony dyssynchrony 0.201950622475474 1 yes impersonal
## 17 VDE-A synchrony dyssynchrony 0.23120492741628 1 yes impersonal
## 18 NLO-B convergence convergence -0.443954182939341 0 yes close
## 19 VDE-B convergence convergence -0.276028480202935 0 yes impersonal
## 20 VDE-A convergence convergence -0.31549012844638 0 yes impersonal
## 21 SGB-B convergence divergence 0.438803836732088 1 yes impersonal
## 22 HUJ-A synchrony dyssynchrony 0.357298884252694 0 yes close
## 23 QRT-A synchrony synchrony -0.390678503584988 1 yes close
## 24 VDE-A synchrony dyssynchrony 0.33594454744154 0 yes impersonal
## 25 AML-B synchrony dyssynchrony 0.30879429857356 0 yes impersonal
## 26 TTY-A synchrony dyssynchrony 0.424723946652559 0 yes close
## 27 HAG-A synchrony synchrony -0.404067610268608 1 yes close
## 28 TTY-B convergence convergence -0.220872291596236 0 yes close
## 29 OAL-A convergence convergence -0.324655159586839 0 yes impersonal
## 30 QRT-B convergence divergence 0.241788295665581 1 yes close
## 31 SUK-A synchrony dyssynchrony 0.183856357146092 0 yes impersonal
## 32 SUK-B synchrony dyssynchrony 0.21883773693464 0 yes impersonal
## 33 ZNV-A synchrony dyssynchrony 0.342883979389005 0 yes close
## 34 AML-B synchrony dyssynchrony 0.231186577537179 0 yes impersonal
## 35 NLO-A synchrony dyssynchrony 0.338057672770634 0 yes close
## 36 OXQ-B synchrony dyssynchrony 0.30774262109949 0 yes impersonal
## 37 TTN-A synchrony dyssynchrony 0.237696697413486 0 yes close
## 38 NLO-A synchrony dyssynchrony 0.274685348789403 0 yes close
## 39 TTN-A synchrony dyssynchrony 0.269606087931864 0 yes close
## feature section
## 1 f0median entireExp
## 2 f0median entireExp
## 3 f0sd entireExp
## 4 f0sd entireExp
## 5 f0median entireExp
## 6 f0median entireExp
## 7 f0max entireExp
## 8 f0max entireExp
## 9 f0max entireExp
## 10 f0max entireExp
## 11 f0max entireExp
## 12 f0max entireExp
## 13 f0max entireExp
## 14 f0sd entireExp
## 15 f0sd entireExp
## 16 f0sd entireExp
## 17 f0sd entireExp
## 18 f0max Lists
## 19 f0max Lists
## 20 f0sd Lists
## 21 f0sd Lists
## 22 f0median Lists
## 23 f0median Lists
## 24 f0max Lists
## 25 f0sd Lists
## 26 f0sd Lists
## 27 f0sd Lists
## 28 f0median Diapix
## 29 f0sd Diapix
## 30 f0sd Diapix
## 31 f0median Diapix
## 32 f0median Diapix
## 33 f0median Diapix
## 34 f0max Diapix
## 35 f0max Diapix
## 36 f0max Diapix
## 37 f0max Diapix
## 38 f0sd Diapix
## 39 f0sd DiapixMost effects were when we take the entire experiment. But a considerable number of effects in each section too. (Note: the higher number of effects across the whole experiment should not be because of the increased number of datapoints, since we do a robustness check with 10 pseudo-datapoints to control for number of points.)
table(all$section[all$entrain=="yes"])##
## Diapix entireExp Lists
## 12 17 10table(paste(all$section[all$entrain=="yes"], all$type[all$entrain=="yes"]))##
## Diapix convergence Diapix synchrony entireExp convergence
## 3 9 4
## entireExp synchrony Lists convergence Lists synchrony
## 13 4 6There is a similar number of divergence and convergence effects. But there was almost no synchrony and a lot of dyssynchrony.
table(all$direction[all$entrain=="yes"])##
## convergence divergence dyssynchrony synchrony
## 7 4 26 2There was roughly the same number of effects on each feature.
table(all$feature[all$entrain=="yes"])##
## f0max f0median f0sd
## 14 10 15table(paste(all$feature[all$entrain=="yes"], all$direction[all$direction!="dyssynchrony" & all$entrain=="yes"]))##
## f0max convergence f0max divergence f0max synchrony
## 7 3 4
## f0median convergence f0median divergence f0median synchrony
## 5 3 2
## f0sd convergence f0sd divergence
## 9 6When a given speaker shows an effect, they more likely also show other effects.
sort(table(all$speaker[all$entrain=="yes"]))##
## HAG-A HBR-B NLO-B QRT-A QRT-B SGB-B SUK-B TTY-B ZNV-A ZNV-B HUJ-A SUK-A TTY-A
## 1 1 1 1 1 1 1 1 1 1 2 2 2
## VDE-B OAL-A OXQ-B VDE-A AML-B NLO-A TTN-A
## 2 3 3 3 4 4 4hist(table(all$speaker[all$entrain=="yes"]))
Exploring relationships between effects above and other variables
Temperature change
Convergence
There doesn’t seem to be a relationship between significant temperature change and a participant’s tendency to converge or diverge from their partner. (Looking at the data set itself, there also doesn’t seem to be any patterns.)
load(paste0(here::here(), "/data/individualTemp.RData"))
t1 <- indTemp |>
filter(section=="entireExp") |>
group_by(speaker) |>
pivot_wider(names_from = ROI, values_from = effect, id_cols=speaker) |>
mutate(section = "entireExp")
t2 <- indTemp |>
filter(section=="Lists") |>
group_by(speaker) |>
pivot_wider(names_from = ROI, values_from = effect, id_cols=speaker) |>
mutate(section = "Lists")
t3 <- indTemp |>
filter(section=="Diapix") |>
group_by(speaker) |>
pivot_wider(names_from = ROI, values_from = effect, id_cols=speaker) |>
mutate(section = "Diapix")
# mutate(section = "Lists") # naming the Diapix section and "Lists" to pair these values with the f0 adaptation during "Lists", since temperature change takes a while to happen
# this also didn't bring about any new insights
t <- rbind(t1, rbind(t2, t3))
# t <- rbind(t1, t3)
dat <- merge(t,
all |>
filter(type == "convergence") |>
select(speaker, direction, entrain, condition, feature, section),
by=c("speaker", "section"), all=TRUE) |>
mutate(conv = case_when(
entrain == "no" ~ "ns",
entrain == "yes" ~ direction
)) |>
mutate_all(as.factor)
dat$Forehead <- relevel(dat$Forehead, ref="ns")
dat$Cheeks <- relevel(dat$Cheeks, ref="ns")
dat$Eyes <- relevel(dat$Eyes, ref="ns")
dat$Nose <- relevel(dat$Nose, ref="ns")
dat$conv <- relevel(dat$conv, ref="ns")ggplot(dat |> drop_na(), aes(Forehead, fill=conv))+
geom_bar()+
ggtitle("Forehead")+
scale_fill_manual(values = c("divergence" = "lightblue", "convergence" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Forehead ~ conv, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 355.950382
## iter 10 value 248.441879
## iter 20 value 248.367432
## final value 248.362748
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0732 0.346 -7.33 2.25e-13
## 2 decrease convconvergence 0.000327 88.0 -0.0912 9.27e- 1
## 3 decrease convdivergence 0.0000939 367. -0.0253 9.80e- 1
## 4 increase (Intercept) 0.625 0.118 4.31 1.61e- 5
## 5 increase convconvergence 0.194 0.845 -1.69 9.15e- 2
## 6 increase convdivergence 0.643 1.16 0.505 6.13e- 1ggplot(dat |> drop_na(), aes(Cheeks, fill=conv))+
geom_bar()+
ggtitle("Cheeks")+
scale_fill_manual(values = c("divergence" = "lightblue", "convergence" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Cheeks ~ conv, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 355.950382
## iter 10 value 324.150740
## final value 324.133640
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.271 0.149 -6.66 2.70e-11
## 2 decrease convconvergence 0.000629 29.3 -0.252 8.01e- 1
## 3 decrease convdivergence 0.573 1.23 0.238 8.12e- 1
## 4 increase (Intercept) 0.335 0.134 -5.14 2.78e- 7
## 5 increase convconvergence 0.444 0.847 -0.267 7.89e- 1
## 6 increase convdivergence 0.498 1.23 -0.00801 9.94e- 1ggplot(dat |> drop_na(), aes(Eyes, fill=conv))+
geom_bar()+
ggtitle("Eyes")+
scale_fill_manual(values = c("divergence" = "lightblue", "convergence" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Eyes ~ conv, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 299.921155
## iter 10 value 206.435788
## iter 20 value 206.339052
## final value 206.332662
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0792 0.368 -6.66 2.76e-11
## 2 decrease convconvergence 1.00 65.9 0.164 8.69e- 1
## 3 decrease convdivergence 0.357 842. -0.000698 9.99e- 1
## 4 increase (Intercept) 0.638 0.130 4.37 1.26e- 5
## 5 increase convconvergence 1.00 65.9 0.135 8.93e- 1
## 6 increase convdivergence 1.00 180. 0.0623 9.50e- 1ggplot(dat |> drop_na(), aes(Nose, fill=conv))+
geom_bar()+
ggtitle("Nose")+
scale_fill_manual(values = c("divergence" = "lightblue", "convergence" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Nose ~ conv, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 355.950382
## iter 10 value 274.873198
## final value 274.849731
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.362 0.203 -2.79 5.23e- 3
## 2 decrease convconvergence 0.000292 54.9 -0.148 8.82e- 1
## 3 decrease convdivergence 0.999 29.2 0.250 8.02e- 1
## 4 increase (Intercept) 0.756 0.140 8.06 7.35e-16
## 5 increase convconvergence 0.446 0.848 -0.255 7.99e- 1
## 6 increase convdivergence 0.999 29.2 0.230 8.18e- 1Synchrony
Also for synchrony, there doesn’t seem to be a relationship between temperature change and tendency to (dys)synchronize.
dat <- merge(t,
all |>
filter(type == "synchrony") |>
select(speaker, direction, entrain, condition, feature, section),
by=c("speaker", "section"), all=TRUE) |>
mutate(syn = case_when(
entrain == "no" ~ "ns",
entrain == "yes" ~ direction
)) |>
mutate_all(as.factor)
dat$Forehead <- relevel(dat$Forehead, ref="ns")
dat$Cheeks <- relevel(dat$Cheeks, ref="ns")
dat$Eyes <- relevel(dat$Eyes, ref="ns")
dat$Nose <- relevel(dat$Nose, ref="ns")
dat$syn <- relevel(dat$syn, ref="ns")ggplot(dat |> drop_na(), aes(Forehead, fill=syn))+
geom_bar()+
ggtitle("Forehead")+
scale_fill_manual(values = c("dyssynchrony" = "lightblue", "synchrony" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Forehead ~ syn, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 355.950382
## iter 10 value 247.683020
## iter 20 value 247.528105
## iter 30 value 247.517939
## iter 30 value 247.517939
## iter 30 value 247.517939
## final value 247.517939
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0598 0.390 -7.07 1.59e-12
## 2 decrease syndyssynchrony 0.611 1.12 0.403 6.87e- 1
## 3 decrease synsynchrony 1.00 49.9 0.212 8.32e- 1
## 4 increase (Intercept) 0.621 0.121 4.07 4.71e- 5
## 5 increase syndyssynchrony 0.461 0.431 -0.362 7.17e- 1
## 6 increase synsynchrony 0.999 49.9 0.147 8.83e- 1ggplot(dat |> drop_na(), aes(Cheeks, fill=syn))+
geom_bar()+
ggtitle("Cheeks")+
scale_fill_manual(values = c("dyssynchrony" = "lightblue", "synchrony" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Cheeks ~ syn, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 355.950382
## iter 10 value 324.174085
## iter 20 value 324.105678
## iter 30 value 324.102335
## final value 324.102283
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.256 0.156 -6.83 8.39e-12
## 2 decrease syndyssynchrony 0.610 0.494 0.908 3.64e- 1
## 3 decrease synsynchrony 0.744 1.42 0.751 4.53e- 1
## 4 increase (Intercept) 0.339 0.136 -4.92 8.58e- 7
## 5 increase syndyssynchrony 0.429 0.543 -0.528 5.97e- 1
## 6 increase synsynchrony 0.00129 38.8 -0.171 8.64e- 1ggplot(dat |> drop_na(), aes(Eyes, fill=syn))+
geom_bar()+
ggtitle("Eyes")+
scale_fill_manual(values = c("dyssynchrony" = "lightblue", "synchrony" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Eyes ~ syn, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 299.921155
## iter 10 value 208.026588
## iter 20 value 207.905687
## iter 30 value 207.897841
## final value 207.897838
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.0978 0.351 -6.33 2.44e-10
## 2 decrease syndyssynchrony 0.000513 47.4 -0.160 8.73e- 1
## 3 decrease synsynchrony 0.000358 114. -0.0699 9.44e- 1
## 4 increase (Intercept) 0.654 0.136 4.70 2.64e- 6
## 5 increase syndyssynchrony 0.481 0.464 -0.168 8.67e- 1
## 6 increase synsynchrony 0.0000867 55.2 -0.169 8.66e- 1ggplot(dat |> drop_na(), aes(Nose, fill=syn))+
geom_bar()+
ggtitle("Nose")+
scale_fill_manual(values = c("dyssynchrony" = "lightblue", "synchrony" = "red", "ns" = "gray"))
tidy(m <- nnet::multinom(Nose ~ syn, data=dat)) |>
as_tibble() |>
mutate(estimate = plogis(estimate)) # plogis transforms log odds (the output of the model) into probabilities## # weights: 12 (6 variable)
## initial value 355.950382
## iter 10 value 272.621431
## final value 272.573611
## converged## # A tibble: 6 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 decrease (Intercept) 0.347 0.212 -2.98 2.86e- 3
## 2 decrease syndyssynchrony 0.759 0.761 1.51 1.32e- 1
## 3 decrease synsynchrony 0.00230 20.2 -0.300 7.64e- 1
## 4 increase (Intercept) 0.757 0.144 7.89 2.96e-15
## 5 increase syndyssynchrony 0.646 0.643 0.938 3.48e- 1
## 6 increase synsynchrony 0.000145 33.3 -0.265 7.91e- 1Questionnaire scores
Using regression coefficient
d <- merge(all |> filter(type == "convergence") |> select(-condition),
meta,
by="speaker", all=TRUE) |>
# mutate(entrain = ifelse(is.na(entrain), "no", entrain),
# direction = ifelse(is.na(direction), "no", direction),
# type = ifelse(is.na(type), "convergence", type)) |>
mutate(effect = case_when(
entrain == "no" ~ "ns",
entrain == "yes" ~ direction
)) |>
mutate_at(c("entrain", "direction", "type", "effect"), as.factor) |>
mutate_at(c("coefficient"), as.numeric)
d$effect <- relevel(d$effect, ref="ns")No effect of condition.
summary(lmer(coefficient ~ condition + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ condition + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -263.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.87861 -0.63383 0.05659 0.57917 3.04691
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005086 0.07132
## Residual 0.022974 0.15157
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.02081 0.01968 34.18966 1.057 0.298
## conditionimpersonal -0.02845 0.02849 33.74052 -0.998 0.325
##
## Correlation of Fixed Effects:
## (Intr)
## cndtnmprsnl -0.691No effect of speakers’ perception of their partner. (It was the same
null result if I used d |> filter(entrain=="yes"), and
also if we look at each section separately.)
summary(lmer(coefficient ~ closeness + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ closeness + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -260.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8805 -0.6228 0.0539 0.5706 3.0579
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005175 0.07193
## Residual 0.022966 0.15155
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.016168 0.031799 33.637379 -0.508 0.614
## closeness 0.006355 0.007712 33.637379 0.824 0.416
##
## Correlation of Fixed Effects:
## (Intr)
## closeness -0.893summary(lmer(coefficient ~ similarity + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ similarity + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -261.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.79069 -0.63298 0.04363 0.56102 3.13755
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.004847 0.06962
## Residual 0.022995 0.15164
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.068524 0.046637 32.981787 1.469 0.151
## similarity -0.010891 0.007903 33.009544 -1.378 0.177
##
## Correlation of Fixed Effects:
## (Intr)
## similarity -0.954summary(lmer(coefficient ~ likeability + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ likeability + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -260.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84476 -0.62292 0.04601 0.57262 3.07541
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005307 0.07285
## Residual 0.022971 0.15156
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.0106562 0.0704852 33.7361786 0.151 0.881
## likeability -0.0004988 0.0100570 33.8773281 -0.050 0.961
##
## Correlation of Fixed Effects:
## (Intr)
## likeability -0.979summary(lmer(coefficient ~ becomeFriends + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ becomeFriends + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -260.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.80597 -0.62520 0.03825 0.56254 3.07642
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005161 0.07184
## Residual 0.022979 0.15159
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.044295 0.051058 33.404863 0.868 0.392
## becomeFriends -0.006244 0.008258 33.614280 -0.756 0.455
##
## Correlation of Fixed Effects:
## (Intr)
## becomeFrnds -0.960# summary(lmer(coefficient ~ closeness + (1|speaker), d |> filter(section=="Lists")))
# summary(lmer(coefficient ~ similarity + (1|speaker), d |> filter(section=="Lists")))
# summary(lmer(coefficient ~ likeability + (1|speaker), d |> filter(section=="Lists")))
# summary(lmer(coefficient ~ becomeFriends + (1|speaker), d |> filter(section=="Lists")))
#
# summary(lmer(coefficient ~ closeness + (1|speaker), d |> filter(section=="Diapix")))
# summary(lmer(coefficient ~ similarity + (1|speaker), d |> filter(section=="Diapix")))
# summary(lmer(coefficient ~ likeability + (1|speaker), d |> filter(section=="Diapix")))
# summary(lmer(coefficient ~ becomeFriends + (1|speaker), d |> filter(section=="Diapix")))
#
# summary(lmer(coefficient ~ closeness + (1|speaker), d |> filter(section=="entireExp")))
# summary(lmer(coefficient ~ similarity + (1|speaker), d |> filter(section=="entireExp")))
# summary(lmer(coefficient ~ likeability + (1|speaker), d |> filter(section=="entireExp")))
# summary(lmer(coefficient ~ becomeFriends + (1|speaker), d |> filter(section=="entireExp")))No effect of speakers’ perception of BFI scores.
summary(lmer(coefficient ~ extraversion + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ extraversion + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -262
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8338 -0.6216 0.0562 0.5791 3.0690
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005281 0.07267
## Residual 0.022970 0.15156
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.023629 0.084964 33.424880 -0.278 0.783
## extraversion 0.008929 0.024225 33.438679 0.369 0.715
##
## Correlation of Fixed Effects:
## (Intr)
## extraversin -0.986summary(lmer(coefficient ~ openness + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ openness + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -261.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83197 -0.61228 0.03945 0.56462 3.07429
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.005254 0.07249
## Residual 0.022970 0.15156
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.039064 0.064183 33.833267 0.609 0.547
## openness -0.009011 0.017706 33.679235 -0.509 0.614
##
## Correlation of Fixed Effects:
## (Intr)
## openness -0.975summary(lmer(coefficient ~ agreeableness + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ agreeableness + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -262.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84765 -0.62133 0.05057 0.57654 3.07567
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00531 0.07287
## Residual 0.02297 0.15156
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.0002254 0.0996640 34.1385172 -0.002 0.998
## agreeableness 0.0021606 0.0285599 34.3035338 0.076 0.940
##
## Correlation of Fixed Effects:
## (Intr)
## agreeablnss -0.989summary(lmer(coefficient ~ conscientiousness + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ conscientiousness + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -264.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.79128 -0.63232 0.04264 0.55342 3.10992
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00471 0.06863
## Residual 0.02299 0.15162
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.16748 0.09903 32.98984 1.691 0.100
## conscientiousness -0.03986 0.02439 32.97684 -1.634 0.112
##
## Correlation of Fixed Effects:
## (Intr)
## conscntsnss -0.990summary(lmer(coefficient ~ neuroticism + (1|speaker), d))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: coefficient ~ neuroticism + (1 | speaker)
## Data: d
##
## REML criterion at convergence: -262.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84933 -0.61298 0.04392 0.57265 3.09676
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0.00517 0.0719
## Residual 0.02297 0.1516
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05777 0.06400 33.57066 0.903 0.373
## neuroticism -0.01601 0.01976 33.49477 -0.810 0.424
##
## Correlation of Fixed Effects:
## (Intr)
## neuroticism -0.975Using yes/no labels
Convergence
d <- merge(all |> filter(type == "convergence") |> select(-condition),
meta,
by="speaker", all=TRUE) |>
# mutate(entrain = ifelse(is.na(entrain), "no", entrain),
# direction = ifelse(is.na(direction), "no", direction),
# type = ifelse(is.na(type), "convergence", type)) |>
mutate(effect = case_when(
entrain == "no" ~ "ns",
entrain == "yes" ~ direction
)) |>
mutate_at(c("entrain", "direction", "type", "effect"), as.factor)
d$effect <- relevel(d$effect, ref="ns")Condition: no effect
summary(glmer(entrain ~ condition + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ condition + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 101.8 113.2 -47.9 95.8 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2125 -0.2125 -0.1534 -0.1534 6.5192
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 3.84e-16 1.96e-08
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.7495 0.5058 -7.412 1.24e-13 ***
## conditionimpersonal 0.6520 0.6365 1.024 0.306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## cndtnmprsnl -0.795
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Scores on questionnaire about perception of partner: no effect. Also no effect when we take each section separately.
summary(glmer(entrain ~ closeness + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ closeness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 100.5 111.9 -47.2 94.5 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2978 -0.2058 -0.1608 -0.1422 7.0335
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.3943 0.7836 -5.608 2.04e-08 ***
## closeness 0.2465 0.1598 1.543 0.123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## closeness -0.920
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ similarity + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ similarity + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 102.6 114.0 -48.3 96.6 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2142 -0.1950 -0.1860 -0.1693 6.1903
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.92830 1.07776 -3.645 0.000268 ***
## similarity 0.09411 0.17513 0.537 0.591001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## similarity -0.959
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ likeability + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ likeability + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 100.7 112.2 -47.4 94.7 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2510 -0.2121 -0.1793 -0.1281 9.2391
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 6.306e-14 2.511e-07
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.7927 1.8122 -3.196 0.00139 **
## likeability 0.3365 0.2402 1.401 0.16123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## likeability -0.985
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ becomeFriends + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ becomeFriends + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 102.8 114.3 -48.4 96.8 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.1920 -0.1867 -0.1841 -0.1815 5.6654
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.553 1.117 -3.182 0.00146 **
## becomeFriends 0.028 0.179 0.156 0.87574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## becomeFrnds -0.962
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# summary(glmer(entrain ~ closeness + (1|speaker), d |> filter(section=="Lists"), family=binomial))
# summary(glmer(entrain ~ similarity + (1|speaker), d |> filter(section=="Lists"), family=binomial))
# summary(glmer(entrain ~ likeability + (1|speaker), d |> filter(section=="Lists"), family=binomial))
# summary(glmer(entrain ~ becomeFriends + (1|speaker), d |> filter(section=="Lists"), family=binomial))
#
# summary(glmer(entrain ~ closeness + (1|speaker), d |> filter(section=="Diapix"), family=binomial))
# summary(glmer(entrain ~ similarity + (1|speaker), d |> filter(section=="Diapix"), family=binomial))
# summary(glmer(entrain ~ likeability + (1|speaker), d |> filter(section=="Diapix"), family=binomial))
# summary(glmer(entrain ~ becomeFriends + (1|speaker), d |> filter(section=="Diapix"), family=binomial))BFI scores: no effect, BUT openness, agreeableness, neuroticism not converging.
summary(glmer(entrain ~ extraversion + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ extraversion + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 102.6 114.0 -48.3 96.6 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2082 -0.1945 -0.1849 -0.1727 6.3012
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4605 1.7953 -1.371 0.171
## extraversion -0.2713 0.5254 -0.516 0.606
##
## Correlation of Fixed Effects:
## (Intr)
## extraversin -0.985
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ openness + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ openness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 101.5 112.9 -47.7 95.5 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2487 -0.2137 -0.1767 -0.1518 7.9640
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 4.296e-17 6.554e-09
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.0612 1.5459 -3.274 0.00106 **
## openness 0.4557 0.3978 1.146 0.25196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## openness -0.980
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ agreeableness + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ agreeableness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 101.7 113.1 -47.8 95.7 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2416 -0.1987 -0.1716 -0.1556 6.7493
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0811 2.1290 -0.508 0.612
## agreeableness -0.6844 0.6401 -1.069 0.285
##
## Correlation of Fixed Effects:
## (Intr)
## agreeablnss -0.990
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ conscientiousness + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ conscientiousness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 102.0 113.5 -48.0 96.0 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2275 -0.1975 -0.1793 -0.1639 7.7404
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.5815 2.5399 -2.198 0.028 *
## conscientiousness 0.5359 0.6050 0.886 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## conscntsnss -0.993
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(glmer(entrain ~ neuroticism + (1|speaker), d, family=binomial))## boundary (singular) fit: see help('isSingular')## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ neuroticism + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 102.4 113.8 -48.2 96.4 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.2257 -0.1949 -0.1812 -0.1653 6.6299
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 0 0
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.3327 1.4431 -3.002 0.00268 **
## neuroticism 0.2931 0.4271 0.686 0.49256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## neuroticism -0.977
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Direction of the effect (convergence vs divergence)
Condition: no effect
tidy(m <- nnet::multinom(effect ~ condition, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 66.821060
## iter 20 value 54.974000
## final value 54.912750
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.0173 0.582 -6.93 4.16e-12
## 2 convergence conditionimpersonal 0.594 0.772 0.492 6.22e- 1
## 3 divergence (Intercept) 0.00585 1.00 -5.12 3.04e- 7
## 4 divergence conditionimpersonal 0.767 1.16 1.03 3.05e- 1No effect of perception of partner
tidy(m <- nnet::multinom(effect ~ closeness, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 83.751097
## iter 20 value 54.223578
## final value 54.200834
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.00525 1.04 -5.03 0.000000496
## 2 convergence closeness 0.582 0.201 1.65 0.0996
## 3 divergence (Intercept) 0.00835 1.17 -4.08 0.0000443
## 4 divergence closeness 0.525 0.264 0.375 0.707tidy(m <- nnet::multinom(effect ~ similarity, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 94.167561
## iter 20 value 57.155099
## final value 55.322567
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.00776 1.42 -3.41 0.000653
## 2 convergence similarity 0.543 0.224 0.770 0.441
## 3 divergence (Intercept) 0.0148 1.63 -2.57 0.0102
## 4 divergence similarity 0.491 0.282 -0.129 0.897tidy(m <- nnet::multinom(effect ~ likeability, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 56.277056
## iter 20 value 54.482283
## final value 54.481685
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.00111 2.38 -2.86 0.00420
## 2 convergence likeability 0.601 0.310 1.32 0.186
## 3 divergence (Intercept) 0.00265 2.76 -2.15 0.0316
## 4 divergence likeability 0.554 0.374 0.581 0.561tidy(m <- nnet::multinom(effect ~ becomeFriends, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 77.488071
## iter 20 value 55.665221
## final value 55.627293
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.0179 1.39 -2.88 0.00401
## 2 convergence becomeFriends 0.507 0.223 0.125 0.900
## 3 divergence (Intercept) 0.0103 1.83 -2.49 0.0128
## 4 divergence becomeFriends 0.507 0.294 0.0952 0.924BFI scores: no effects
tidy(m <- nnet::multinom(effect ~ extraversion, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 56.871466
## iter 20 value 55.454628
## final value 55.453741
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.0765 2.24 -1.11 0.267
## 2 convergence extraversion 0.402 0.665 -0.598 0.550
## 3 divergence (Intercept) 0.0149 2.95 -1.42 0.155
## 4 divergence extraversion 0.485 0.845 -0.0698 0.944tidy(m <- nnet::multinom(effect ~ openness, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 61.037843
## iter 20 value 54.279508
## final value 54.278538
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.000931 2.19 -3.19 0.00143
## 2 convergence openness 0.696 0.537 1.54 0.122
## 3 divergence (Intercept) 0.0176 2.19 -1.84 0.0661
## 4 divergence openness 0.473 0.616 -0.174 0.862tidy(m <- nnet::multinom(effect ~ agreeableness, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 56.833729
## iter 20 value 54.602195
## final value 54.593083
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.0493 2.64 -1.12 0.262
## 2 convergence agreeableness 0.436 0.770 -0.334 0.738
## 3 divergence (Intercept) 0.662 3.71 0.181 0.856
## 4 divergence agreeableness 0.175 1.18 -1.31 0.189tidy(m <- nnet::multinom(effect ~ conscientiousness, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 67.863695
## iter 20 value 54.988666
## final value 54.983220
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.00758 2.92 -1.67 0.0947
## 2 convergence conscientiousness 0.564 0.707 0.362 0.717
## 3 divergence (Intercept) 0.000107 4.94 -1.85 0.0643
## 4 divergence conscientiousness 0.757 1.14 0.995 0.320tidy(m <- nnet::multinom(effect ~ neuroticism, data=d)) |>
as_tibble() |>
mutate(estimate = plogis(estimate))## # weights: 9 (4 variable)
## initial value 369.133729
## iter 10 value 59.259551
## iter 20 value 55.039171
## final value 55.039107
## converged## # A tibble: 4 × 6
## y.level term estimate std.error statistic p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 convergence (Intercept) 0.00326 1.91 -2.99 0.00281
## 2 convergence neuroticism 0.639 0.545 1.05 0.296
## 3 divergence (Intercept) 0.0206 2.18 -1.77 0.0763
## 4 divergence neuroticism 0.457 0.692 -0.249 0.804Synchrony
d <- merge(all |> filter(type == "synchrony") |> select(-condition),
meta,
by="speaker", all=TRUE) |>
# mutate(entrain = ifelse(is.na(entrain), "no", entrain),
# direction = ifelse(is.na(direction), "no", direction),
# type = ifelse(is.na(type), "synchrony", type)) |>
mutate(effect = case_when(
entrain == "no" ~ "ns",
entrain == "yes" ~ direction
)) |>
mutate_at(c("entrain", "direction", "type", "effect"), as.factor) No effect of condition on dyssynchrony.
summary(glmer(entrain ~ condition + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ condition + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.7 197.2 -89.9 179.7 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6841 -0.2852 -0.1584 -0.1583 3.5069
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.166 1.472
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.209422 0.614452 -5.223 1.76e-07 ***
## conditionimpersonal 0.001684 0.701146 0.002 0.998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## cndtnmprsnl -0.577No effect of perception of partner.
summary(glmer(entrain ~ closeness + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ closeness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.2 196.7 -89.6 179.2 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7049 -0.2788 -0.1609 -0.1449 3.5874
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.187 1.479
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.7318 0.9526 -3.918 8.94e-05 ***
## closeness 0.1364 0.1949 0.700 0.484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## closeness -0.850summary(glmer(entrain ~ similarity + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ similarity + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 184.2 195.6 -89.1 178.2 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7030 -0.2614 -0.1666 -0.1403 4.0585
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.082 1.443
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8582 1.1204 -1.659 0.0972 .
## similarity -0.2441 0.2012 -1.213 0.2250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## similarity -0.901summary(glmer(entrain ~ likeability + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ likeability + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.4 196.9 -89.7 179.4 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6973 -0.2759 -0.1646 -0.1510 3.7280
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.136 1.461
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.3480 1.6818 -1.396 0.163
## likeability -0.1255 0.2408 -0.521 0.602
##
## Correlation of Fixed Effects:
## (Intr)
## likeability -0.955summary(glmer(entrain ~ becomeFriends + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ becomeFriends + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.4 196.8 -89.7 179.4 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7037 -0.2773 -0.1643 -0.1522 3.7917
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.139 1.462
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5479 1.2067 -2.111 0.0347 *
## becomeFriends -0.1116 0.1939 -0.576 0.5648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## becomeFrnds -0.911No effect of BFI scores.
summary(glmer(entrain ~ extraversion + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ extraversion + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.7 197.1 -89.8 179.7 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6904 -0.2847 -0.1599 -0.1557 3.5419
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.17 1.473
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.5648 2.1056 -1.693 0.0904 .
## extraversion 0.1025 0.5838 0.176 0.8607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## extraversin -0.971summary(glmer(entrain ~ openness + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ openness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 184.7 196.2 -89.4 178.7 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7003 -0.2606 -0.1645 -0.1531 4.0379
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.002 1.415
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.6444 1.6096 -2.885 0.00391 **
## openness 0.4118 0.4167 0.988 0.32298
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## openness -0.953summary(glmer(entrain ~ agreeableness + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ agreeableness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 182.9 194.4 -88.5 176.9 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7299 -0.2601 -0.1781 -0.1242 4.1805
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.019 1.421
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7887 2.4373 0.324 0.746
## agreeableness -1.1713 0.7402 -1.582 0.114
##
## Correlation of Fixed Effects:
## (Intr)
## agreeablnss -0.981summary(glmer(entrain ~ conscientiousness + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ conscientiousness + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.6 197.1 -89.8 179.6 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6875 -0.2764 -0.1610 -0.1554 3.6182
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.153 1.467
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4564 2.4059 -1.021 0.307
## conscientiousness -0.1870 0.5918 -0.316 0.752
##
## Correlation of Fixed Effects:
## (Intr)
## conscntsnss -0.978summary(glmer(entrain ~ neuroticism + (1|speaker), d, family=binomial))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: entrain ~ neuroticism + (1 | speaker)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 185.5 197.0 -89.8 179.5 333
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6889 -0.2719 -0.1610 -0.1537 3.7687
##
## Random effects:
## Groups Name Variance Std.Dev.
## speaker (Intercept) 2.124 1.458
## Number of obs: 336, groups: speaker, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5229 1.5947 -1.582 0.114
## neuroticism -0.2156 0.4885 -0.441 0.659
##
## Correlation of Fixed Effects:
## (Intr)
## neuroticism -0.950(It doesn’t make sense to analyze the direction of the effect here—snychrony vs. dyssynchrony—because there were almost no synchrony effects.)
(I also wanted to link the temperature and speech datasets to analyze them in relation to each other. But there are so few speech data points with significant entrainment, that it doesn’t make sense to make this analysis.)
Session Info
sessionInfo()## R version 4.2.3 (2023-03-15 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8
## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
## [5] LC_TIME=German_Germany.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] emmeans_1.8.9 parsnip_1.1.1 broom_1.0.5 DHARMa_0.4.6
## [5] lmerTest_3.1-3 lme4_1.1-30 Matrix_1.5-3 lubridate_1.9.2
## [9] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
## [13] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3
## [17] tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.2 jsonlite_1.8.7 splines_4.2.3
## [4] here_1.0.1 bslib_0.4.0 highr_0.9
## [7] broom.mixed_0.2.9.4 yaml_2.3.5 globals_0.16.2
## [10] numDeriv_2016.8-1.1 pillar_1.9.0 backports_1.4.1
## [13] lattice_0.20-45 glue_1.6.2 digest_0.6.29
## [16] minqa_1.2.4 hardhat_1.3.0 colorspace_2.0-3
## [19] sandwich_3.0-2 htmltools_0.5.3 pkgconfig_2.0.3
## [22] listenv_0.9.0 xtable_1.8-4 mvtnorm_1.1-3
## [25] scales_1.2.1 tzdb_0.3.0 timechange_0.2.0
## [28] mgcv_1.8-40 farver_2.1.1 generics_0.1.3
## [31] ellipsis_0.3.2 furrr_0.3.1 TH.data_1.1-1
## [34] cachem_1.0.6 withr_2.5.0 nnet_7.3-19
## [37] cli_3.6.1 survival_3.5-3 magrittr_2.0.3
## [40] estimability_1.4.1 evaluate_0.23 parallelly_1.34.0
## [43] future_1.30.0 fansi_1.0.3 nlme_3.1-162
## [46] MASS_7.3-58.2 tools_4.2.3 hms_1.1.2
## [49] lifecycle_1.0.3 multcomp_1.4-23 munsell_0.5.0
## [52] compiler_4.2.3 jquerylib_0.1.4 rlang_1.1.1
## [55] grid_4.2.3 nloptr_2.0.3 rstudioapi_0.15.0
## [58] labeling_0.4.2 rmarkdown_2.16 boot_1.3-28.1
## [61] gtable_0.3.1 codetools_0.2-19 R6_2.5.1
## [64] zoo_1.8-10 knitr_1.40 fastmap_1.1.0
## [67] utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.8
## [70] parallel_4.2.3 Rcpp_1.0.9 vctrs_0.6.3
## [73] tidyselect_1.2.0 xfun_0.32 coda_0.19-4